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Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionFor years, self-service analytics apps have enabled both information consumers (business users) and information workers (analysts) to meet their need for data assets that aid analysis and problem-solving. These data assets can include ready-made insights and analysis in the form of statistics, visual stories, or formatted data for further discovery. Historically, for an enterprise to embark on creating analytics apps, it required a specialized skillset, technology tools, and a steep learning curve to deliver value.Three significant trends have shifted how we view analytics apps today:●  No-code and low-code data acquisition, along with cloud data/warehouse platforms, have helped democratize the data platform.●  Data platforms like Snowflake are designed to bring analytics computing into a single platform where data no longer needs to be copied and moved.●  The democratization of machine learning and the widespread availability of powerful generative AI models have changed the entire user experience and expectations for information discovery and natural language exploration.The result of these trends has accelerated technology cycles and the rate of innovation in unprecedented ways. Prudent technology and business leaders are strained with more requests and fewer resources to use data to build information-focused businesses.Currently, we have AI app and analytics waves breaking at the same time with different use cases in mind but the same objective. For this article, we wanted to explore the basics of building a simple analytics app inside of Snowflake, allowing an OpenAI interface to execute code without ever accessing any of the resulting data.Modern Data Cloud and Analytics Technology ToolsLet us explore the process and benefits of building an LLM-powered application using a cloud-based data warehousing platform like Snowflake and an open-source Python library for creating web applications like Streamlit. Ref: https://www.snowflake.com/blog/building-python-data-apps-streamlit/Understanding Snowflake Data Warehousing Snowflake is a leading cloud data platform offering secure and scalable solutions for processing and storing data. The architecture of Snowflake allows easy integration with programming languages. It eventually works on data-intensive applications. To work with Snowflake, one must create a Snowflake account to set up the database for data storage.LLM Powered Inputs and TranslationEvery large language model, including GPT-4, is capable of understanding and generating human-like texts based on prompts and inputs it receives. These models are trained on vast datasets, enabling them to comprehend large and complex language patterns and generate contextually relevant responses. An incredible aspect of large language models, particularly GPT-4, is their ability to effectively translate natural language into code, including SQL and Python.Large language models are not designed for computational procedures like statistics and analytics, but with the right prompting and, most importantly, context, you can streamline many common tasks.Integration of Snowflake with Python and Streamlit SnowparkIn data analysis and machine learning (ML), Python is the most versatile programming language. Snowflake offers a Python connector that enables seamless communication between Snowflake databases and Python scripts. In this article, we are not using Snowpark.Storyboarding our AppThe difference between a good app and a great app lies in the value you create for your user. The secret to building a great app is empowering users to solve problems that would otherwise be painful or impossible due to a lack of skills. The app we are building here demonstrates how to fit technology components together.Minimum Viable Product Storyboard:●  End user: Analytics app developer●  Intent: Demonstrate core tech components●  Outcome: Have●  Value: Quickly understand a functional code example without having to researchWe will build a native Streamlit app inside of Snowflake:●  The app will feature a chat interface powered by ChatGPT.●  The chat history will be written on a Snowflake table.●  The GPT model will read the results of a simple query, interpret the results, and summarize them in plain English.Bringing Technology Components TogetherFor this article, we decided to build a simple end-to-end demonstration of how a native Snowflake app built with Python and Streamlit can utilize a chatbot interface that uses ChatGPT-4 to generate SQL code that can be executed natively in Snowflake with the context of the schema.Snowflake Integration of ChatGPT Large Language Model APITo receive responses with the help of a large language model, leverage the OpenAI Documentation and Playground. Obtain the OpenAI GPT Key, and then use the following code to interact with a large language model.-- Step 1 - Create a Secret for open ai key . CREATE OR REPLACE SECRET open_ai_api_key TYPE = GENERIC_STRING SECRET_STRING = '<OPEN_AI_KEY>'; -- Step 2 - Create a Network rule on Snowflake CREATE OR REPLACE NETWORK RULE openai_network_rule MODE = EGRESS TYPE = HOST_PORT VALUE_LIST = ('api.openai.com'); -- Step 3 Create a EXTERNAL ACCESS INTEGRATION in Snowflake CREATE OR REPLACE EXTERNAL ACCESS INTEGRATION external_access_int ALLOWED_NETWORK_RULES = (openai_network_rule) ALLOWED_AUTHENTICATION_SECRETS = (open_ai_api_key) ENABLED = true; -- Step 4 Create a UDF using openai packages . Here we are using "gpt-3.5-turbo" Model CREATE OR REPLACE FUNCTION CHATGPTv1(query varchar) RETURNS STRING LANGUAGE PYTHON RUNTIME_VERSION = 3.9 HANDLER = 'runner' EXTERNAL_ACCESS_INTEGRATIONS = (external_access_int) SECRETS = ('openai_key' = open_ai_api_key) PACKAGES = ('openai') AS $$ import _snowflake import openai def runner(QUERY):    openai.api_key = _snowflake.get_generic_secret_string('openai_key')    messages = [{"role": "user", "content": QUERY}]    model="gpt-3.5-turbo"    response = openai.ChatCompletion.create(model=model,messages=messages,temperature=0,)    return response.choices[0].message["content"] $$; -- Test your UDF SELECT CHATGPTv1('Hi')Creation of Streamlit User Experience InterfaceTo create the Streamlit user experience the following code was utilized to build a very basic functional prototype with GPT3.5 Turbo.1. Installation:pip install Streamlit2. Creation:from snowflake.snowpark.context import get_active_session st.set_page_config(layout="wide") st.title("OPEN AI IN SIS - GPT-3.5-turbo(MODEL)") st.write("##") st.write("##") # Get the current credentials session = get_active_session() if 'request_response' not in st.session_state:    st.session_state['request_response'] = {} if st.session_state['request_response']:    for itr in st.session_state['request_response'].keys():        request_col , request_col1 = st.columns(2)        response_col1 , response_col = st.columns(2)        with request_col:            st.write(f":bust_in_silhouette:  :blue[{itr}]")        st.write("##")        with response_col:            st.write(f":speech_balloon:  :red[{st.session_state['request_response'][itr][0]}]") col1 ,col2 = st.columns(2) with col1:    search_text= st.text_input("Send a message")    search_button = st.button("Send") if search_text and search_button:    search_result = session.sql(f"SELECT CHATGPTv1('{search_text}')").collect()    if search_result:        st.session_state['request_response'][search_text] = [search_result[0][0]]        st.experimental_rerun()3. Run:Streamlit run app.pyMoving from MVP to Real-World ApplicationReal-world analytics apps are designed with a narrow scope, outcome, and value in mind. Let's expand on the same technology components and formulate a real-world use case that will be more impactful to an enterprise. When evaluating real-world business cases to apply Streamlit and OpenAI, focus on use cases that deliver value frequently, to many (or important) people in your organization, and are tied to high-impact business processes.Data Tape Co-pilot Tool:●  End user: Financial Analysts, Business Analysts, Data Analysts.●  Intent: Deliver a data tape with the ability to constrain data to business needs and provide a basic summary.●  Outcome: End users can download the data tape and receive a plain English summary of key stats (record count, distinct key, constraints in the query contained in the WHERE clause).●  Value: Provide natural language access to a single, widely used data tape with a clear, plain English explanation of the dataset.Streamlit Analytics Improves User Adoption and Success with Snowflake With a better understanding of Streamlit as a driver for the adoption of Snowflake and the increasing adoption of data assets, let's dig deeper into Streamlit as the conduit for adoption. While Snowflake may be a known entity within your enterprise, few business-facing professionals will ever know they are interfacing with Snowflake, and that is okay. Without more technology tools and platforms, Streamlit opens the doors to Snowflake but most importantly eliminates other tools, platforms, and an additional layer of services to manage. Instead, you can leverage the skills already on hand within most data and analytics teams. Here are some additional features that make Streamlit quite compelling:●  Simplicity and Ease of Use: Streamlit provides an intuitive API that allows developers to create interactive UI elements with minimal code. Its straightforward syntax enables both beginners and experienced developers to quickly prototype and deploy applications without a steep learning curve.●  Rapid Prototyping: Streamlit excels at rapid prototyping, enabling developers to iterate quickly on their ideas. With its live reloading feature, developers can see changes in real time as they modify the code. This development speed is crucial for experimenting with different UI layouts and functionalities.●  Data Exploration and Visualization: Streamlit integrates seamlessly with popular data science libraries . Some of these are Pandas, Matplotlib, and Plotly. This integration allows developers to create dynamic and interactive charts, graphs, and dashboards with minimal effort. Data scientists and analysts can effectively showcase their findings, making it an excellent choice for data exploration and visualization tasks.●  Customization and Theming: While Streamlit provides a simple interface, it also offers customization options for developers who want to create visually appealing applications. Developers can customize the appearance of their apps, including layout, colors, and themes, to match their brand or specific design preferences.●  Seamless Integration with Machine Learning and AI Models: Streamlit makes integrating machine learning models, natural language processing tools, and other AI technologies into applications easy. Developers can create interactive interfaces for AI-powered applications, enabling users to interact with complex algorithms and models without understanding the underlying complexities.●  Sharing and Deployment: Streamlit apps can be easily shared and deployed on various platforms. Whether it's sharing within a team, showcasing a prototype to stakeholders, or deploying a full-fledged application for public use, Streamlit simplifies the process. Streamlit sharing, Streamlit's deployment platform, allows developers to deploy apps with minimal configuration, making them accessible to a broader audience.●  Active Community and Documentation: Streamlit has a vibrant and active community of developers. The availability of numerous examples, tutorials, and community-contributed components enhances the development experience. Streamlit's comprehensive documentation provides detailed guidance on various aspects of building interactive applications, making it easier for developers to find solutions to their queries.●  Flexibility and Extensibility: While Streamlit is easy for beginners, it also offers flexibility and extensibility for advanced users. Developers can create custom components and integrate JavaScript functionality when needed, allowing them to extend Streamlit's capabilities based on their requirements.ConclusionThe integration of Snowflake and Streamlit offers a powerful combination for building analytics and data delivery apps. A single, blended data warehousing solution with intuitive application development can democratize data access, enabling users across an organization to transform complex datasets into palatable, prepared information assets. Though the Snowflake modern data cloud app store is in its infancy, you can jump in today and seize a great opportunity to build powerful data apps. While this article explained a simple GPT API interface, the recent introduction of GPT Assistants API expands the possibilities for even more intelligent, contextual agents running securely running right where you work. I look forward to expanding on this basic prototype to a more intelligent co-pilot experience soon.Author BioRyan Goodman has dedicated 20 years to the business of data and analytics, working as a practitioner, executive, and entrepreneur. He recently founded DataTools Pro after 4 years at Reliant Funding, where he served as the VP of Analytics and BI. There, he implemented a modern data stack, utilized data sciences, integrated cloud analytics, and established a governance structure. Drawing from his experiences as a customer, Ryan is now collaborating with his team to develop rapid deployment industry solutions. These solutions utilize machine learning, LLMs, and modern data platforms to significantly reduce the time to value for data and analytics teams.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!👋 Hello ,“The real power that AI brings to education is connecting our learning intelligently to make us smarter in the way we understand ourselves, the world and how we teach and learn.” - Rose Luckin, UCL professor, Co-founder, Institute for Ethical AI in Education AI makes learning more inclusive and personalized than ever before. Recent advancements including the launch of Microsoft’s AI-powered Reading Coach and OpenAI’s first-of-its-kind partnership with the Arizona State University will ensure the future of learning is bright. Welcome back to a new issue of AI Distilled - your one-stop destination for all things AI, ML, NLP, and Gen AI. Let’s get started with the latest news and developments across sectors: To begin with, 💎 Explore Packt's New Year, New Data Upskilling program – Meet the Datapro Mini Library: an essential, user-friendly platform you can't afford to overlook. AI Launches & Industry Updates:  AI Will Not Displace Humans Anytime Soon, Says MIT Study Voice Cloning Startup ElevenLabs Raises $80 Million, Achieves Unicorn Status Samsung Introduces New AI Features in Galaxy Phones AI Graphic Design Startup Recraft Raises $12 Million OpenAI CEO Looking to Establish Own AI Chip Factories Meta CEO Mark Zuckerberg Enters Race to Build AGI AI in Education: OpenAI Signs Deal with Arizona State University Microsoft Makes AI-Powered Reading Coach Freely Available AI in Healthcare:  AI to Save Asia-Pacific Healthcare $100 Billion Annually by 2025 WHO Releases Guidance on Ensuring Ethics of Powerful AI Models AI in Finance: Survey Finds Majority of Finance Leaders Believe AI Will Boost Productivity Singapore Fintech Startup Secures Series A Funding to Automate Accounting AI in Supply Chain Management: AI and Supply Chain Changes Top Priorities for Apparel Brands in 2024 We’ve also curated the latest GPT and LLM resources, tutorials, and secret knowledge:  Discover New Methods for Aligning Chatbots New Framework Helps AI Systems Evaluate Their Own Answers Making Sense of Time: Understanding the Mathematical Underpinnings of Recurrent Neural Networks Detecting Deception: New Methods to Uncover AI Untruths Finally, don’t forget to check-out our hands-on tips and strategies from the AI community for you to use on your own projects:  How to Use RAGxplorer to Help Make Sense of AI Data How to Create a Multi-Modal Nutrition Tool How to Combine Language Models Using Raspberry Pi with Offline Speech and Language Models Looking for some inspiration? Here are some GitHub repositories to get your projects going!  huggingface/nanotron tencentarc/visft linkdd/aitoolkit FlagOpen/TACO  📥 Feedback on the Weekly EditionTake our weekly survey and get a free PDF copy of our best-selling book, "Interactive Data Visualization with Python - Second Edition." 📣 And here's the twist – we're tuning into YOUR frequency! Inspired by a reader's request, we're launching a column just for you. Got a burning question or a topic you're itching to dive into? Drop your suggestions in our content box – because your journey of discovery is our blueprint.We appreciate your input and hope you enjoy the book! Share your thoughts and opinions here! Writer’s Credit: Special shout-out to Vidhu Jain for their valuable contribution to this week’s newsletter content!  Cheers,  Merlyn Shelley  Editor-in-Chief, Packt  SignUp | Advertise | Archives✨Packt's 2024 Specials✨Discover Packt's New Year, New Data Upskilling program, designed for data professionals. Gain a competitive edge in data science and analytics with expert-curated resources. Our goal? To help you seamlessly upgrade your skills in the most efficient way possible, enabling you to switch between topics without losing your stride. Introducing the Datapro Mini Library: a smooth, user-friendly platform that you simply can't afford to miss. Here’s what our DataPro platform offers: On-Demand Learning: Immerse yourself in Packt’s comprehensive data-based knowledge base, featuring hundreds of books, video courses, research papers, and articles. Expert Problem Solving: Get bespoke solutions to your most challenging problems, directly from our vast network of data experts and authors. Advanced Self-Assessment: Utilize our tools for skill gap analysis and progress tracking, pinpointing areas for improvement and tracking your learning journey. Personalized DataPro Dashboard: Keep tabs on your activities, revisit recent learning sections, and receive tailored recommendations to align with your learning objectives. Skill Gap Analysis: Deep dive into your SQL, R, Python, and other skills with detailed quizzes and personalized feedback. The icing on the cake? Join the thriving community of more than 150 data/AI professionals in our Discord channel. Get exclusive access to our DataPro beta program, and even have a chance to win Amazon gift cards! All this is available for just $7.99 per month. Remember Benjamin Franklin's words, "An investment in knowledge pays the best interest." There’s no better time to invest in your professional growth than now. Don't miss this opportunity to power up your data journey. Subscribe now and take the first step towards becoming a data mastermind!Sign Up Here ⚡ TechWave: AI/GPT News & AnalysisAI Launches & Industry Updates💎 AI Will Not Displace Humans Anytime Soon, Says MIT Study: A MIT study explored the potential impact of AI, particularly computer vision, on jobs involving visual analysis. The findings suggest that only 23% of wages in these jobs are cost-effective to automate with current AI. Job displacement is expected to be gradual, taking decades to significantly affect employment levels, contrary to some earlier predictions. 💎 Voice Cloning Startup ElevenLabs Raises $80 Million, Achieves Unicorn Status: ElevenLabs, a voice AI startup, secured $80 million in Series B funding, reaching a $1 billion valuation. Their tech creates realistic voices from text or samples, targeting audiobooks, dubbing, and gaming. While investors highlight its potential, ethical and legal concerns persist regarding voice cloning. 💎 Samsung Introduces New AI Features in Galaxy Phones: Samsung's Galaxy S24 smartphones now offer AI translation features with up to 13 languages. Users can call, text, and translate live audio and text using Google's Gemini AI model, ensuring private and secure on-device translations, aiding international communication and travelers. 💎 AI Graphic Design Startup Recraft Raises $12 Million: London's Recraft, an AI graphic design startup, secures $12 million in Series A funding, led by Khosla Ventures and Nat Friedman. Their platform helps brands create visuals from text prompts. With 300,000 users, Recraft aims to develop its own graphic design foundation model, potentially reducing the need for designers as the global design AI market is expected to reach $7.75 billion by 2032. 💎 OpenAI CEO Looking to Establish Own AI Chip Factories: OpenAI CEO Sam Altman is seeking billions in investment, including $8 billion from G42, to establish his own AI-specific ASIC factories due to concerns about semiconductor foundries' ability to meet future AI chip demand. This move aims to secure OpenAI's access to specialized AI processors and promote industry self-reliance in chip design and manufacturing. 💎 Meta CEO Mark Zuckerberg Enters Race to Build AGI: Meta CEO Mark Zuckerberg aims to develop artificial general intelligence (AGI), bolstered by 600,000 GPUs by 2024. He plans to integrate AGI into Meta apps and share models openly, though closure is an option if safety or strategic concerns arise in the pursuit of superhuman intelligence. AI in Education💎 OpenAI Signs Deal with Arizona State University: OpenAI signed a deal with Arizona State University to bring its ChatGPT AI chatbot to ASU researchers, staff and faculty. This indicates shifting views on using AI in education as the technology advances. AI has potential benefits for helping students but concerns about plagiarism linger largely unaddressed. 💎 Microsoft Makes AI-Powered Reading Coach Freely Available: Microsoft offers free access to its AI-based Reading Coach for users with Microsoft accounts. The tool offers personalized reading practice with features like text-to-speech, but experts emphasize the irreplaceable role of teachers in assessing comprehension. AI in Healthcare💎 AI to Save Asia-Pacific Healthcare $100 Billion Annually by 2025: IDC predicts generative AI will save 10% of clinician time in Asia-Pacific (excluding Japan) by 2025, leading to $100 billion in healthcare savings. By 2027, half of healthcare organizations will double AI investments for personalized care. Other forecasts include 30% adopting virtualized work models by 2025 and 60% emphasizing "techquity" partnerships to bridge digital divides. IDC anticipates the next five years shaping a patient-centric, AI-driven healthcare future in the region. 💎 WHO Releases Guidance on Ensuring Ethics of Powerful AI Models: The WHO releases guidelines for Large Multi-Modal Models (LMMs) in healthcare, highlighting their potential and risks. Over 40 recommendations address responsible development, oversight, and equitable use, emphasizing diversity and safety to protect users and promote health equity. AI in Finance💎 Survey Finds Majority of Finance Leaders Believe AI Will Boost Productivity: A survey by OneStream found 80% of financial decision-makers believe AI will increase productivity in finance departments within five years. AI streamlines data management and improves forecasting, despite challenges like training and data privacy. Finance leaders see AI as a key part of their operations. 💎 Singapore Fintech Startup Secures Series A Funding to Automate Accounting: Singapore-based AI accounting startup Bluesheets secured $6.5 million in a Series A round led by Illuminate Financial Management, with support from Antler. Bluesheets, founded in 2020, uses ML to simplify financial workflows for businesses, serving 10,000+ customers globally. Despite generating $180,000 in revenue last year, the company incurred $2.39 million in losses while expanding its platform. AI in Supply Chain Management💎 AI and Supply Chain Changes Top Priorities for Apparel Brands in 2024: A survey of 250 apparel and fashion executives reveals that top tech priorities include using AI for marketing and financial forecasting. Many plan to increase onshoring and invest in automation, while also opening and closing stores to focus on smaller formats.  🔮 Expert Insights from Packt Community 💎 Unlocking the Secrets of Prompt Engineering - By Gilbert Mizrahi "Unlocking the Secrets of Prompt Engineering" is your go-to guide to mastering AI-driven writing with large language models (LLMs). Learn prompt fundamentals, apply LLMs for content creation, chatbots, and coding. Explore practical use cases, from product descriptions to creative writing. Dive into advanced applications, ethics, and best practices. Unlock AI's full potential in writing and boost productivity. Get your copy now and transform your writing skills with AI. 💎 Building LLM Apps - By Valentina Alto This is your comprehensive guide to Large Language Models (LLMs). It covers LLM fundamentals, architectural frameworks like GPT 3.5/4 and Falcon LLM, and introduces LangChain. Learn to create intelligent agents, retrieve unstructured data, and engage with structured data using LLMs. Explore the future of Large Foundation Models (LFMs) extending AI capabilities beyond language. Whether you're an AI expert or newcomer, this book is your roadmap to unleash the power of LLMs. Access the book now and shape the future of intelligent machines. 💎 Machine Learning for Time Series - Second Edition - By Ben Auffarth This latest book offers an elaborative guide to Python time-series packages, aiding in the creation of predictive systems. Covering traditional autoregressive models to modern non-parametric ones, this edition explains loading time-series data, deep learning, convolutional networks, and gradient boosting. New additions include financial market forecasting and case studies. Master time-series analysis with machine learning. Take the first step towards mastering time series analysis - get your copy now.  🌟 Secret Knowledge: AI/LLM Resources💎 Discover New Methods for Aligning Chatbots: Hugging Face researchers tested three methods to enhance conversational AI assistants without reinforcement learning: Direct Preference Optimization, Identity Preference Optimization, and Kahneman-Tversky Optimization. Tuning hyperparameters, especially beta, proved crucial for better performance in multi-turn conversations. 💎 New Framework Helps AI Systems Evaluate Their Own Answers: Google researchers created ASPIRE to enhance LLMs' self-confidence assessment. It fine-tunes models and trains them to self-evaluate. Test results show ASPIRE improves error identification and smaller models using it outperform larger ones. It's a step toward more trustworthy AI in decision-making. 💎 Making Sense of Time: Understanding the Mathematical Underpinnings of Recurrent Neural Networks: Discover the math behind Recurrent Neural Networks (RNNs), which excel in analyzing sequences like time series. The author explains RNN equations, shows how to build one from scratch in Python, and demonstrates their use in predicting stock prices, revealing their ability to capture time-based patterns. 💎 Detecting Deception: New Methods to Uncover AI Untruths: Researchers at Kolena used various methods to spot inaccuracies in LLM-generated responses. They achieved over 90% accuracy in detecting errors with context. More techniques like self-consistency testing and involving another AI improved accuracy.  🔛 Masterclass: AI/LLM Tutorials💎 How to Use RAGxplorer to Help Make Sense of AI Data: Discover RAGxplorer, a web app for understanding AI data. Upload documents to see how they're analyzed in chunks and their connections to questions. It unveils insights into retrieval-augmented generation (RAG) and is a promising tool for exploring AI training datasets. 💎 How to Create a Multi-Modal Nutrition Tool: Learn how to develop a smart food journal to help track nutrition and diet goals. The journal allows users to take pictures of meals which are then analyzed using GPT-4 Vision to provide nutritional information. Autogen helps rapidly build the application by leveraging LLMs. A user-friendly interface was created with Gradio. 💎 How to Combine Language Models: Combine ML models to create a versatile AI. The article explains techniques like weighted averaging and dealing with parameter conflicts. Learn to merge Mistral, WizardMath, and CodeLlama using the mergekit toolkit. 💎 Raspberry Pi with Offline Speech and Language Models: Discover how to enable AI on a Raspberry Pi without internet. Learn to make the tiny device understand and respond to speech using locally stored LLMs. The article guides setting up Whisper and fine-tuning GPT-2 on the Pi, showing an affordable offline AI solution.  🚀 HackHub: Trending AI Tools💎 huggingface/nanotron: Tools for efficiently distributing LLM training across multiple processors via 3D parallelism techniques. 💎 tencentarc/visft: Two-stage training technique called ViSFT to improve large foundation models on visual tasks. 💎 linkdd/aitoolkit: The AI Toolkit library provides C++ tools like finite state machines, behavior trees, utility AI and goal-oriented action planning to help developers create intelligent non-player characters for their games.  💎 FlagOpen/TACO: Topics in Algorithmic COde generation is a dataset containing over 25,000 programming problems to evaluate state-of-the-art models.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!This article is an excerpt from the book, ChatGPT for Accelerating Salesforce Development, by Andy Forbes, Philip Safir, Joseph Kubon, Francisco Fálder. Harness ChatGPT for streamlined flows, effective configuration, proficient code writing, and enhanced project activities.IntroductionEmbark on a journey through Salesforce wizardry in this article as we delve into the art of optimizing Flows for email functionality within the Snowflake data realm. Picture this: you, the savvy sales rep, armed with a wizard that guides you through the intricate process of selecting the perfect contact for your email endeavors. Join us in dissecting a case study, navigating through user stories, and tackling challenges head-on. We'll explore the nitty-gritty details, gaps, and tricks to ensure a seamless Salesforce experience. Get ready to enhance your Flow game and witness the magic unfold in the world of Salesforce efficiency!Enhancing Email Selection Wizards for Sales EfficiencyThe case study from Appendix A has several user stories that can be addressed with Flows.Let’s use the base flow request:“Please provide detailed directions on how to create a Salesforce Flow to address the following user story. Please note any gaps in the user story that leave an opportunity for misunderstandings, as well as any parts of the flow that may be tricky to create. Include error handling in the Flow, and write a description of what the Flow does for the project documentation.”We’ll also use epic 3 and user story 2 from the case study to investigate ChatGPT’s capabilities.Epic 3 – email functionality with Snowflake dataUser Story 2 – create a Flow-based wizard to select a contact:• Goal: As a sales rep, I want a wizard to guide me through the process of selecting a contact to send the email to so that I can choose the correct recipient.• Acceptance criteria:‚ Given a sales rep clicks the Email button in the Snowflake section‚ When the wizard starts‚ Then the sales rep should be prompted to select a contact associated with the accountLet’s take a look:Prompt:Please provide detailed directions on how to create a Salesforce flow to address the following user story. Please note any gaps in the user story that leave an opportunity for misunderstandings, as well as any parts of the flow that may be tricky to create. Include error handling in the flow, and write a description of what the Flow does for the project documentation.Epic 3 – email functionality with Snowflake dataUser story 2 – create a Flow-based wizard to select a contact:•    Goal: As a sales rep, I want a wizard to guide me through the process of selecting a contact to send the email to so that I can choose the correct recipient•    Acceptance criteria:‚    Given a sales rep clicks the Email button in the Snowflake section‚    When the wizard starts‚    Then the sales rep should be prompted to select a contact associated with the accountResponse: Certainly! Let’s address the user story and design a Flow-based wizard to select a contact for emailing in Salesforce. We’ll also examine any potential gaps and challenges in the process.Flow design for email functionality with Snowflake data:1. Start the Flow:Trigger: The Flow will be triggered by clicking an Email button within the Snowflake sectionAction: A wizard screen will appear, prompting the sales rep to proceed with selecting a contact associated with the account2. Screen Element for Contact Selection:Create a screen element with a dropdown that lists contacts associated with the accountThis list should be dynamically generated based on the account related to the Snowflake section3. Error handling and validation:Include logic to check if there are any contacts associated with the account If no contacts are found, an error message should be displayed to the sales rep4. Finish the Flow:Once the contact has been selected, the wizard should end, and the selection should be available for further processing, such as sending an email Gaps and challenges:Ambiguity in the Snowflake section: The story does not clearly define what the “Snowflake section” refers to. Understanding its context is essential to properly link the Email button to the flow.Selection process clarity: The story does not provide details on what happens after contact selection, which might require collaboration with other stakeholders for complete understanding.Account association: Ensuring that the listed contacts are relevant to the specific account can be tricky and needs careful handling.Project documentationFlow name: Contact selection wizard for Snowflake email functionality.Description: This Salesforce flow enables sales reps to select a contact associated with an account within the Snowflake section by guiding them through a wizard. Triggered by clicking the Email button, the Flow provides a drop-down list of relevant contacts for selection, includes error handling to deal with scenarios where no contacts are found and ends with the selected contact being available for email communication.Associated epic: Epic 3 – email functionality with Snowflake data.User story: User story 2 – create a Flow-based wizard to select a contact.Goal: To facilitate the correct selection of an email recipient by guiding the sales rep through a structured process.By carefully considering these steps, challenges, and potential gaps, this Flow aims to provide an intuitive and error-free way for sales reps to select the appropriate contact for their email communications within the Snowflake section. It underscores the importance of alignment with stakeholders to ensure the full context and intended behavior of the Flow are understood and accurately implemented.ChatGPT identified three gaps and challenges. Let’s prompt ChatGPT to refine the answer based on answers to the gaps and challenges:Prompt:The Snowflake section on the contact page layout is a tab dedicated to displaying information from Snowflake. There are additional user stories that address what happens after this user story. The contacts to be listed are the ones where the account lookup field on the contact is all the same account. With these answers, please update and rewrite the response. Anything unaffected by these answers does not need to change. Response:Certainly! With the additional context provided, let’s refine the Flow design to create a Salesforce Flow-based wizard that aligns with the user story and its requirements.Flow design for email functionality with Snowflake data:1. Start the Flow:Trigger: The flow will be triggered by clicking an Email button within the Snowflake tab on the contact page layoutAction: A wizard screen will appear, prompting the sales rep to proceed with selecting a contact associated with the account2. Screen element for contact selection:Create a screen element with a dropdown that lists contacts associated with the same account, where the account lookup field on the contact matches the specific accountThis list should be dynamically generated based on the account related to theSnowflake tab3. Error handling and validation:Include logic to check if there are any contacts associated with the specific account. If no contacts are found, an error message should be displayed to the sales rep4. Finish the Flow:Once the contact has been selected, the wizard should end, and the selection should be available for further processing, as defined in the subsequent user stories.Gaps and challenges:Account association: Ensuring that the listed contacts are relevant to the specific account and filtered by the account lookup field can be tricky and needs careful handling.Project documentationFlow name: Contact selection wizard for Snowflake email functionality.Description: This Salesforce flow enables sales reps to select a contact associated with an account within the Snowflake tab on the contact page layout. Triggered by clicking the Email button, the flow provides a drop-down list of contacts where the account lookup field matches the specific account. It includes error handling to deal with scenarios where no contacts are found and ends with the selected contact being available for further processing as detailed in subsequent user stories.Associated Epic: Epic 3 – mail functionality with Snowflake data.User Story: User story 2 – create a Flow-based wizard to select a contact.Goal: To facilitate the correct selection of an email recipient by guiding the sales rep through a structured process within the Snowflake tab.By integrating the details about the Snowflake section and the specific Account relationship, this refined flow design aims to provide a seamless experience for Sales Reps in selecting the appropriate contact for email communications. It emphasizes the precision needed in filtering contacts based on the Account lookup field, thus catering to the specific requirements of the given user story.Even though the “Account Association” concern seems to have been addressed, ChatGPT is still listing it as a concern. This could be a ChatGPT quirk or there could still be a gap. Let’s directly ask for an explanation:Prompt: Under “Gaps and Challenges” is Account Association: ensuring that the listed contacts are relevant to the specific account and filtered by the account lookup field can be tricky and needs careful handling.I thought I addressed this by telling you that “The contacts to be listed are the ones where the account lookup field on the contact are all the same account.”Why is this tricky? What is needed to resolve this “Gap and Challenge”?Response:My apologies for the confusion. Your direction does indeed clarify the requirement, but let me further explain why I considered this as a potential challenge.The “tricky” part I mentioned relates to the design and implementation phase of the flow, specifically in the context of Salesforce. Here’s why:1. Query complexity: Retrieving contacts based on the specific account lookup field requires a well-constructed SOQL query. While it’s a straightforward requirement, the developer must ensure that the query is efficient, especially if there are large numbers of contacts related to an account.2. Contextual handling: The Flow must recognize the specific account related to the Snowflake tab from which the Email button is clicked. This requires proper handling of contextual information, ensuring that the correct account ID is used in filtering the contacts.3. Dynamic filtering: If the Account field is ever updated, or if the context changes dynamically within the user interface, the flow must respond accordingly, updating the list of contacts. Ensuring that this dynamic filtering works smoothly can require careful testing and validation.4. Permissions and security: Ensuring that the user has the appropriate permissions to view and select the contacts related to the specific account must be considered.This involves compliance with the Salesforce org’s security model.Resolution:1. Construct a robust SOQL query: Build a well-designed SOQL query to fetch contacts based on the specific account lookup field.2. Contextual Handling: Utilize the context of the button click within the Snowflake tab to identify the relevant account, possibly using Apex if necessary.3. Test dynamically: Implement tests to ensure that the dynamic filtering of contacts works as expected as the context changes.4. Consider security: Align the flow with the Salesforce org’s security protocols to ensure that it adheres to the relevant permissions and profiles.In the hands of an experienced Salesforce developer like yourself, these challenges would be well within reach to overcome. I mentioned them to highlight potential areas that might require attention, and they can be seen as aspects to validate rather than insurmountable obstacles.And since the authors want to get along with our AI overlords, here’s the prompt we provided:Prompt:Thank you for your explanation of how to address this “Gap and Challenge” as well as for your help with this user story!Response:You’re welcome! It’s been a pleasure working alongside you on this user story. Your in-depth knowledge and detailed questions have enabled a clear and precise understanding of the requirements. Should you have any more questions or need further assistance, feel free to reach out. Here’s to a successful implementation of the Flow, and may it greatly enhance the functionality and user experience within your Salesforce environment. Happy developing!ConclusionAnd there you have it – a deep dive into the world of Salesforce wizardry! From refining Flows to addressing challenges and optimizing for Snowflake data, we've navigated the intricate landscape of email functionality. Picture your sales team empowered with intuitive wizards, seamlessly selecting contacts for emails. As we wrap up, remember the key: precision in design and collaboration with stakeholders. Let this be a guide for a successful implementation, enhancing your Salesforce environment. Here's to smooth workflows, error-free communication, and a Salesforce experience that truly works its magic for you! Happy developing! Author BioAndy Forbes stands at the vanguard of technological innovation. With a career spanning four decades, he is currently focusing his robust IT background on exploring the transformative potential of artificial intelligence, particularly Generative AI, on Salesforce project delivery. Andy has honed a deep expertise in CRM and project management which is backed by his repertoire of ITIL and Salesforce certifications. Andy's eight-year tenure at a Global Systems Integrator, coupled with his entrepreneurial spirit, has propelled him to the leadership of multiple Salesforce projects for Fortune 500 clients.Philip Safir is a consulting executive and business architect. He serves enterprises via delivery of technology roadmaps, process improvement, and solutions on the Salesforce platform. As the Head of Salesforce Professional Services Delivery & Talent for a Global Systems Integrator, he is responsible for a team of 250 consultants and a $100M portfolio. His career spans Fortune 500, start-up, and international clients across various industry domains like Manufacturing, Retail, Financial Services, Telecom, and so on.Joseph Kubon, an experienced Solution Architect for global enterprise deliveries, Salesforce MVP with 40+ Salesforce certifications and inventor (holding several patents), navigates the realms of manufacturing, health and media industries with a results-driven approach. Skilled in Agile methodologies, Business Process, and Architecture values, he carries a toolkit replete with Salesforce configuration and customization expertise for groundbreaking development. Guided by the wisdom that 'just because it can be built doesn't mean it should be', Joseph embraces the multiplicity of solutions to tomorrow's challenges measuring success with his “Time to Value” principles.Francisco Fálder is a seasoned Salesforce maven and master of digital transformation. His career stands as a testament to a commitment to delivering top-tier, complex projects, seamlessly merging business and tech to deliver standout customer experiences. With every project, Paco re-imagines and redefines the digital landscape, fostering an environment where innovation is not only encouraged but celebrated. Passionate about the ever-evolving tech world, he has honed a deep-rooted affinity for Artificial Intelligence, Continuous Integration/Continuous Deployment (CI/CD), and Agile methodologies.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!👋 Hello ,“This year, every industry will become a technology industry. You can now recognize and learn the language of almost anything with structure, and you can translate it to anything with structure — so text-protein, protein-text. This is the generative AI revolution.” -Jensen Huang, NVIDIA founder and CEO. AI is revolutionizing drug development and reshaping medical tech with cutting-edge algorithms. Dive into the latest AI_Distilled edition for sharp insights on AI's impact across industries, including breakthroughs in machine learning, NLP, and more. AI Launches & Industry Updates:  OpenAI Revises Policy, Opening Doors to Military Applications Google Cloud Introduces Advanced Generative AI Tools for Retail Enhancement Google Confirms Significant Layoffs Across Core Teams OpenAI Launches ChatGPT Team for Collaborative Workspaces Microsoft Launches Copilot Pro Plan and Expands Business Availability Vodafone and Microsoft Forge 10-Year Partnership for Digital Transformation AI in Healthcare:  MIT Researchers Harness AI to Uncover New Antibiotic Candidates Google Research Unveils AMIE: AI System for Diagnostic Medical Conversations NVIDIA CEO Foresees Tech Transformation Across All Industries in 2024 AI in Finance: AI Reshapes Financial Industry: 2024 Trends Unveiled in Survey JPMorgan Seeks AI Strategist to Monitor London Startups AI in Fintech Market to Surpass $222.49 Billion by 2030 AI in Business: AI to Impact 40% Jobs Globally, Balanced Policies Needed, Says IMF Deloitte's Quarterly Survey Reveals Business Leaders' Concerns About Gen AI's Societal Impact and Talent Shortage AI in Science & Technology:  NASA Boosts Scientific Discovery with Generative AI-Powered Search Swarovski Unveils World's First AI Binoculars AI in Supply Chain Management: AI Proves Crucial in Securing Healthcare Supply Chains: Economist Impact Study Unlocking Supply Chain Potential: Generative AI Transforms Operations We’ve also got you your fresh dose of LLM, GPT, and Gen AI secret knowledge and tutorials: How to Craft Effective AI Prompts Understanding and Managing KV Caching for LLM Inference Understanding and Enhancing Chain-of-Thought (CoT) Reasoning with Graphs Unlocking the Power of Hybrid Deep Neural Networks We know how much you love hands-on tips and strategies from the community, so here they are: Building a Local Chatbot with Next.js, Llama.cpp, and ModelFusion How to Build an Anomaly Detector with OpenAI Building Multilingual Financial Search Applications with Cohere Embedding Models in Amazon Bedrock Maximizing GPU Utilization with AWS ParallelCluster and EC2 Capacity Blocks Don’t forget to review these GitHib repositories that have been doing rounds:  vanna-ai/vanna dvmazur/mixtral-offloading pootiet/explain-then-translate genezc/minima   📥 Feedback on the Weekly EditionTake our weekly survey and get a free PDF copy of our best-selling book, "Interactive Data Visualization with Python - Second Edition." We appreciate your input and hope you enjoy the book! Share your thoughts and opinions here! Writer’s Credit: Special shout-out to Vidhu Jain for their valuable contribution to this week’s newsletter content!  Cheers,  Merlyn Shelley  Editor-in-Chief, Packt  SignUp | Advertise | Archives⚡ TechWave: AI/GPT News & AnalysisAI Launches & Industry Updates: 💎 OpenAI Revises Policy, Opening Doors to Military Applications: OpenAI updated its policy, lifting the ban on using its tech for military purposes, aiming for clarity and national security discussions. However, it maintains a strict prohibition against developing and using weapons. 💎 Google Cloud Introduces Advanced Generative AI Tools for Retail Enhancement: Google Cloud has released new AI tools to improve online shopping and help retail businesses. This includes a smart chatbot for websites and apps to help customers, a feature to make product searches better, and tools to improve customer service and speed up listing products. 💎 Google Confirms Significant Layoffs Across Core Teams: Google announced major job cuts affecting its Hardware, core engineering, and Google Assistant teams, totaling around a thousand layoffs in a day. The exact number might be higher, but no total count was provided. 💎 OpenAI Launches ChatGPT Team for Collaborative Workspaces: ChatGPT Team is a plan for teams offering a secure space with advanced models like GPT-4 and DALL·E 3. It includes tools for data analysis and lets users create custom GPTs, ensuring business data remains private. 💎 Microsoft Launches Copilot Pro Plan and Expands Business Availability: Copilot Pro, at $20/month per user, offers enhanced text, command, and image features in Microsoft 365 apps, plus early access to new GenAI models. It's also available for businesses on various Microsoft 365 and Office 365 plans. 💎 Vodafone and Microsoft Forge 10-Year Partnership for Digital Transformation: Vodafone and Microsoft have formed a 10-year partnership to serve over 300 million people in Europe and Africa, using Microsoft's AI to improve customer experiences, IoT, digital services for small businesses, and global data center strategies. AI in Healthcare: 💎 MIT Researchers Harness AI to Uncover New Antibiotic Candidates: MIT researchers have employed deep learning to identify a new class of antibiotic compounds capable of combating drug-resistant bacterium Methicillin-resistant Staphylococcus aureus (MRSA). Published in Nature, the study underscores researchers' ability to unveil the deep-learning model's criteria for antibiotic predictions, paving the way for enhanced drug design. 💎 Google Research Unveils AMIE: AI System for Diagnostic Medical Conversations: Google Research introduces the Articulate Medical Intelligence Explorer (AMIE), an AI system tailored for diagnostic reasoning and conversations in the medical field. AMIE, based on LLMs, focuses on replicating the nuanced and skilled dialogues between clinicians and patients, addressing diagnostic challenges. The system employs a unique self-play simulated learning environment, refining its diagnostic capabilities across various medical conditions. 💎 NVIDIA CEO Foresees Tech Transformation Across All Industries in 2024: Jensen Huang predicts a tech revolution in all industries by 2024, focusing on generative AI's impact. At a healthcare conference, he highlighted AI's role in language and translation, and NVIDIA's shift from aiding drug discovery to designing drugs with computers. AI in Finance: 💎 AI Reshapes Financial Industry: 2024 Trends Unveiled in Survey: NVIDIA's survey reveals 91% of financial companies are adopting or planning to use AI. 55% are interested in generative AI and LLMs, mainly to enhance operations, risk, and marketing. 97% intend to increase AI investments for new uses and workflow optimization. 💎 JPMorgan Seeks AI Strategist to Monitor London Startups: JPMorgan is hiring an 'AI Strategy Consultant' in London to identify and assess startups using Generative AI and LLMs, reporting to the Chief Data and Analytics Officer. This aligns with financial trends like HSBC's launch of Zing, a money transfer app. 💎 AI in Fintech Market to Surpass $222.49 Billion by 2030: The AI in Fintech market, valued at $13.23 billion in 2022, is growing fast. It's improving financial services with data analytics and machine learning, enhancing decision-making and security. It's projected to reach $222.49 billion by 2030, growing at 42.3% annually.  AI in Business: 💎 AI to Impact 40% Jobs Globally, Balanced Policies Needed, Says IMF: The IMF warns that AI affects 40% of global jobs, posing more risks and opportunities in advanced economies than emerging ones. It may increase income inequality, calling for social safety nets, retraining, and AI-focused policies to ensure inclusivity. 💎 Deloitte's Quarterly Survey Reveals Business Leaders' Concerns About Gen AI's Societal Impact and Talent Shortage: Deloitte's new quarterly survey, based on input from 2,800 professionals globally, shows 79% are optimistic about gen AI's impact on their businesses in 3 years. However, over 50% fear it may centralize global economic power and worsen economic inequality.  AI in Science & Technology:  💎 NASA Boosts Scientific Discovery with Generative AI-Powered Search: NASA introduces the Science Discovery Engine, powered by generative AI, simplifying access to its extensive data. Developed by the Open Source Science Initiative (OSSI) and Sinequa, it comprehends 9,000 scientific terms, offers contextual search, and enables natural language queries for 88,000 datasets and 715,000 documents from 128 sources. 💎 Swarovski Unveils World's First AI Binoculars: Swarovski Optik and designer Marc Newson launch AX VISIO, the first AI binoculars. They merge analog optics with AI, instantly identifying 9,000+ species, boasting a camera-like design, and enabling quick photo and video capture through a neural processing unit.  AI in Supply Chain Management: 💎 AI Proves Crucial in Securing Healthcare Supply Chains: Economist Impact Study: A study by Economist Impact, with DP World's support, finds 46% of healthcare firms use AI to predict supply chain issues. Amid geopolitical uncertainties, 39% use "friendshoring" for trade, and 23% optimize suppliers, showcasing industry adaptability. 💎 Unlocking Supply Chain Potential: Generative AI Transforms Operations: About 40% of supply chains invest in Gen AI for knowledge management. It's widely adopted (62%) for sustainability tracking and helps with forecasting, production, risk management, manufacturing design, predictive maintenance, and logistics efficiency.  🔮 Expert Insights from Packt Community Generative AI with LangChain - By Ben Auffarth How do GPT models work? Generative pre-training has been around for a while, employing methods such as Markov models or other techniques. However, language models such as BERT and GPT were made possible by the transformer deep neural network architecture (Vaswani and others, Attention Is All You Need, 2017), which has been a game-changer for NLP. Designed to avoid recursion to allow parallel computation, the Transformer architecture, in different variations, continues to push the boundaries of what’s possible within the field of NLP and generative AI. Transformers have pushed the envelope in NLP, especially in translation and language understanding. Neural Machine Translation (NMT) is a mainstream approach to machine translation that uses DL to capture long-range dependencies in a sentence. Models based on transformers outperformed previous approaches, such as using recurrent neural networks, particularly Long Short-Term Memory (LSTM) networks. The transformer model architecture has an encoder-decoder structure, where the encoder maps an input sequence to a sequence of hidden states, and the decoder maps the hidden states to an output sequence. The hidden state representations consider not only the inherent meaning of the words (their semantic value) but also their context in the sequence. The encoder is made up of identical layers, each with two sub-layers. The input embedding is passed through an attention mechanism, and the second sub-layer is a fully connected feed-forward network. Each sub-layer is followed by a residual connection and layer normalization. The output of each sub-layer is the sum of the input and the output of the sub-layer, which is then normalized. The architectural features that have contributed to the success of transformers are: Positional encoding: Since the transformer doesn’t process words sequentially but instead processes all words simultaneously, it lacks any notion of the order of words. To remedy this, information about the position of words in the sequence is injected into the model using positional encodings. These encodings are added to the input embeddings representing each word, thus allowing the model to consider the order of words in a sequence. Layer normalization: To stabilize the network’s learning, the transformer uses a technique called layer normalization. This technique normalizes the model’s inputs across the features dimension (instead of the batch dimension as in batch normalization), thus improving the overall speed and stability of learning. Multi-head attention: Instead of applying attention once, the transformer applies it multiple times in parallel – improving the model’s ability to focus on different types of information and thus capturing a richer combination of features. This is an excerpt from the book Generative AI with LangChain - By Ben Auffarth and published in Dec ‘23. To see what's inside the book, read the entire chapter here or try a 7-day free trial to access the full Packt digital library. To discover more, click the button below. Read through the Chapter 1 unlocked here...  🌟 Secret Knowledge: AI/LLM Resources💎 How to Craft Effective AI Prompts: Embark on a journey to understand the intricacies of AI prompts and how they can revolutionize creative content generation. Delve into the workings of AI Prompts, powered by NLP algorithms, and uncover the steps involved in their implementation. 💎 Understanding and Managing KV Caching for LLM Inference: Explore the intricacies of KV caching in the inference process of LLMs in this post. The KV cache, storing key and value tensors during token generation, poses challenges due to its linear growth with batch size and sequence length. The post delves into the memory constraints, presenting calculations for popular MHA models. 💎 Understanding and Enhancing Chain-of-Thought (CoT) Reasoning with Graphs: Explore using graphs to advance Chain-of-Thought (CoT) prompting, boosting reasoning in GPT-4. CoT enables multi-step problem-solving, spanning math to puzzles, vital for enhancing language models. 💎 Unlocking the Power of Hybrid Deep Neural Networks: This article explains Hybrid Deep Neural Networks (HDNNs), advanced ML models changing AI. It covers HDNN architecture, uses, benefits, and future trends, including how they combine various neural networks like CNNs, RNNs, and GANs.  🔛 Masterclass: AI/LLM Tutorials💎 Building a Local Chatbot with Next.js, Llama.cpp, and ModelFusion: Discover how to build a chatbot with Next.js, Llama.cpp, and ModelFusion. This tutorial covers setup, using Llama.cpp for LLM inference in C++, and creating a chatbot base with Next.js, TypeScript, ESLint, and Tailwind CSS. 💎 How to Build an Anomaly Detector with OpenAI: Learn to build an anomaly detector for different data types, including text and numbers, that fits into your data pipeline. The guide starts with the importance of anomaly detection and OpenAI's LLM role, using OpenAI and BigQuery.  💎 Building Multilingual Financial Search Applications with Cohere Embedding Models in Amazon Bedrock: Learn to use Cohere's multilingual model on Amazon Bedrock for advanced financial search tools. Unlike traditional keyword-based methods, Cohere uses machine learning for semantic searches in over 100 languages, improving document analysis and information retrieval. 💎 Maximizing GPU Utilization with AWS ParallelCluster and EC2 Capacity Blocks: Discover how to tackle GPU shortages in machine learning with AWS ParallelCluster and EC2 Capacity Blocks. This guide outlines a three-step method: reserve Capacity Block, configure your cluster, and run jobs effectively, including GPU failure management and multi-queue optimization.  🚀 HackHub: Trending AI Tools💎 vanna-ai/vanna: Toolkit for accurate Text-to-SQL generation via LLMs using RAG to interact with SQL databases through chat.  💎 dvmazur/mixtral-offloading: Achieve efficient inference for Mixtral-8x7B models, utilizing mixed quantization with HQQ for attention layers and experts, along with a MoE offloading strategy. 💎 pootiet/explain-then-translate: 2-stage Chain-of-Thought (CoT) prompting technique for program translation to improve translation across various Python-to-X and X-to-X directions. 💎 genezc/minima: Addresses the challenge of distilling knowledge from large teacher LMs to smaller student ones to optimize the capacity gap for effective LM distillation and achieving competitive performance with resource-efficient models. 

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!This article is an excerpt from the book, Vector Search for Practitioners with Elastic, by Bahaaldine Azarmi and Jeff Vestal. Optimize your search capabilities in Elastic by operationalizing and fine-tuning vector search and enhance your search relevance while improving overall search performanceIntroductionEmbark on a culinary journey where innovation meets tradition, as we delve into the intricacies of recipe retrieval with the dynamic trio of BM25, ELSER, and RRF. In the ever-evolving landscape of cooking queries, this article unveils the power of classic full-text search, semantic understanding through ELSER, and robust rank fusion with RRF. Witness the synergy of these techniques as they seamlessly integrate into the RAG system, combining the efficiency of an Elasticsearch retriever with the creativity of the GPT-4 generator. Join us to explore how this transformative blend goes beyond keywords, shaping a personalized, flavorful experience for culinary enthusiasts.You can find Implementing a RAG-enhanced CookBot - Part 1 here.Building the retriever—RRF with ELSERIn the context of our recipe retrieval task, our goal is to maximize the relevance of the returned recipes based on a user’s query. We will utilize a combination of classic full-text search (via BM25), semantic search (with ELSER), and a robust rank fusion method (RRF). This combination allows us to handle more complex queries and return results that align closely with the user’s intent.Let’s consider the following query:GET recipes/_search { "_source": { "includes": [ "name", "ingredient" ] }, "sub_searches": [ { "query": { "bool": { "must": { "match": { "ingredient": "carrot beef" } }, "must_not": { "match": { "ingredient": "onion" } } } } }, { "query": { "text_expansion": { "ml.tokens": { "model_id": ".elser_model_1", "model_text": "I want a recipe from the US west coast with beef" } } } } ], "rank": { "rrf": { "window_size": 50, "rank_constant": 20 } } }This query includes two types of search. The first uses a classic Elasticsearch Boolean search to find recipes that contain both carrot and beef as ingredients, excluding those with onion. This traditional approach ensures that the most basic constraints of the user are met.The second sub_search employs ELSER to semantically expand the query I want a recipe from the US west coast with beef. ELSER interprets this request based on its understanding of language, enabling the system to match documents that may not contain the exact phrase but are contextually related. This allows the system to factor in the more nuanced preferences of the user.The query then employs RRF to combine the results of the two sub_searches. The window_ size parameter is set to 50, meaning the top 50 results from each sub-search are considered. The rank_constant parameter, set to 20, guides the RRF algorithm to fuse the scores from the two sub_searches.Thus, this query exemplifies the effective combination of BM25, ELSER, and RRF. Exploiting the strengths of each allows CookBot to move beyond simple keyword matches and provide more contextually relevant recipes, improving the user experience and increasing the system’s overall utility.Leveraging the retriever and implementing the generatorNow that we have our Elasticsearch retriever set up and ready to go, let’s proceed with the final part of our RAG system: the generator. In the context of our application, we’ll use the GPT-4 model as the generator. We’ll implement the generator in our recipe_generator.py module and then integrate it into our Streamlit application.Building the generatorWe will start by creating a RecipeGenerator class. This class is initialized with an OpenAI API key(find out how to get an OpenAI key at https://help.openai.com/en/articles/4936850where-do-i-find-my-secret-api-key), which is used to authenticate our requests with the GPT-4 model:import openai import json from config import OPENAI_API_KEY class RecipeGenerator: def __init__(self, api_key): self.api_key = api_key openai.api_key = self.api_key Next, we define the generate function in the RecipeGenerator class. This function takes in a recipe as input, and sends it as a prompt to the GPT-4 model, asking it to generate a detailed, step-by-step guide. def generate(self, recipe): prompts = [{"role": "user", "content": json.dumps(recipe)}] instruction = { "role": "system", "content": "Take the recipes information and generate a recipe with a mouthwatering intro and a step by step guide." } prompts.append(instruction) generated_content = openai.ChatCompletion.create( model="gpt-4", messages=prompts, max_tokens=1000 ) return generated_content.choices[0].message.contentThe prompts are formatted as required by the OpenAI API, and the max_tokens parameter is set to 1000 to limit the length of the generated text. The generated recipe is then returned by the function.Integrating the generator into the Streamlit applicationWith our RecipeGenerator class ready, we can now integrate it into our Streamlit application in main.py. After importing the necessary modules and initializing the RecipeGenerator class, we will set up the user interface with a text input field:from recipe_generator import RecipeGenerator from config import OPENAI_API_KEY generator = RecipeGenerator(OPENAI_API_KEY) input_text = st.text_input(" ", placeholder="Ask me anything about cooking")When the user enters a query, we will use the Elasticsearch retriever to get a relevant recipe. We then pass this recipe to the generate function of  RecipeGenerator, and the resulting text is displayed in the Streamlit application (see a video example at https://www.linkedin.com/posts/ bahaaldine_genai-gpt4-elasticsearch-activity-7091802199315394560-TkPY):if input_text: query = { "sub_searches": [ { "query": { "bool": { "must_not": [ { "match": { "ingredient": "onion" } } ] } } }, { "query": { "text_expansion": { "ml.tokens": { "model_id": ".elser_model_1", "model_text": input_text } } } } ], "rank": { "rrf": { "window_size": 50, "rank_constant": 20 } } } recipe = elasticsearch_query(query) st.write(recipe) st.write(generator.generate(recipe))The generator thus works in tandem with the retriever to provide a detailed, step-by-step recipe based on the user’s query. This completes our implementation of the RAG system in a Streamlit application, bridging the gap between retrieving relevant information and generating coherent, meaningful responses.ConclusionIn conclusion, the marriage of BM25, ELSER, and RRF marks a groundbreaking approach to recipe retrieval, reshaping the culinary landscape. The strategic amalgamation of classic search methodologies, semantic comprehension, and robust rank fusion ensures a tailored and enriched user experience. As we bid farewell to this exploration, it's evident that the RAG system, with its Elasticsearch retriever and GPT-4 generator, successfully bridges the gap between information retrieval and creative recipe generation. This synergistic blend not only meets user expectations but surpasses them, offering a harmonious fusion of precision and creativity in the realm of culinary exploration.Author BioBahaaldine Azarmi, Global VP Customer Engineering at Elastic, guides companies as they leverage data architecture, distributed systems, machine learning, and generative AI. He leads the customer engineering team, focusing on cloud consumption, and is passionate about sharing knowledge to build and inspire a community skilled in AI.Jeff Vestal has a rich background spanning over a decade in financial trading firms and extensive experience with Elasticsearch. He offers a unique blend of operational acumen, engineering skills, and machine learning expertise. As a Principal Customer Enterprise Architect, he excels at crafting innovative solutions, leveraging Elasticsearch's advanced search capabilities, machine learning features, and generative AI integrations, adeptly guiding users to transform complex data challenges into actionable insights.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!This article is an excerpt from the book, Vector Search for Practitioners with Elastic, by Bahaaldine Azarmi and Jeff Vestal. Optimize your search capabilities in Elastic by operationalizing and fine-tuning vector search and enhance your search relevance while improving overall search performanceIntroductionEveryone knows the age-old culinary dilemma, “What can I cook with the ingredients I have?” Many people need help when faced with an array of ingredients but a lack of inspiration or knowledge to whip up a dish. This everyday issue was the spark for our idea—CookBot.CookBot is not just any AI. It’s conceived as an advanced culinary assistant that not only suggests recipes based on the available ingredients but also understands the nuances of user queries, adapts to individual dietary preferences and restrictions, and generates insightful culinary recommendations.Our objective was to infuse CookBot with RAG, ELSER, and RRF technologies. These technologies are designed to enhance the semantic understanding of queries, optimize information retrieval, and generate relevant, personalized responses. By harnessing the capabilities of these advanced tools, we aimed for CookBot to be able to provide seamless, context-aware culinary assistance tailored to each user’s unique needs.Figure: CookBot powered by ElasticDataset overview – an introduction to the Allrecipes.com datasetThe Allrecipes.com dataset, in its raw CSV format, is a treasure trove of diverse and detailed culinary information. Thus, it is the perfect foundation to train our CookBot. It houses an extensive range of recipes, each encapsulated in a unique entry brimming with an array of information.You can find and download the dataset here, as it will be used later in the chapter:https://www.kaggle.com/datasets/nguyentuongquang/all-recipesTo illustrate the richness of this dataset, let’s consider a single entry:"group","name","rating","n_rater","n_ reviewer","summary","process","ingredient" "breakfast-and-brunch.eggs.breakfast-burritos","Ham and Cheese Breakfast Tortillas",0,0,44,"This is great for a special brunch or even a quick and easy dinner. Other breakfast meats can be used, but the deli ham is the easiest since it is already fully cooked.","prep: 30 mins,total: 30 mins,Servings: 4,Yield: 4 servings","12 eggs + <U+2153> cup milk + 3 slices cooked ham, diced + 2 green onions, minced + salt and pepper to taste + 4 ounces Cheddar cheese, shredded + 4 (10 inch) flour tortillas + cup salsa" Each entry in the dataset represents a unique recipe and encompasses various fields:group: This is the categorization of the recipe. It provides a general idea about the type and nature of the dish.name: This is the title or name of the recipe. This field straightforwardly tells us what the dish is. • rating and n_rater: These fields indicate the popularity and approval of the dish among users.n_reviewer: This is the number of users who have reviewed the recipe.summary: This is a brief overview or description of the dish, often providing valuable context about its taste, usage, or preparation.process: This field outlines crucial details such as preparation time, total cooking time, servings, and yield.ingredient: This is a comprehensive list of all the ingredients required for the recipe, along with their quantities.The detailed information offered by each field gives us a broad and varied information space, aiding the retriever in navigating the data and ensuring the generator can accurately respond to a diverse range of culinary queries. As we move forward, we will discuss how we indexed this dataset using Elasticsearch, the role of ELSER and RRF in effectively retrieving data, and how the GPT-4 model generates relevant, personalized responses based on the retrieved data.Preparing data for RAG-enhanced searchTo transform the Allrecipes.com data into a searchable database, we first need to parse the CSV file and subsequently create an Elasticsearch index where data will be stored and queried. Let’s walk through this process implemented as part of the Python code.Connecting to ElasticsearchFirst, we need to establish a connection with our Elasticsearch instance. This connection is handled by the Elasticsearch object from the Elasticsearch Python module:from elasticsearch import Elasticsearch es = Elasticsearch()In this case, we assume that our Elasticsearch instance runs locally with default settings. If it doesn’t, we will need to provide the appropriate host and port information to the Elasticsearch class.Defining the indexThe next step is to define an index where our recipes will be stored. An index in Elasticsearch is like a database in traditional database systems. In this case, we’ll call our index recipes:index_name = 'recipes'Creating the mappingNow, we need to create a mapping for our index. A mapping is like a schema in a SQL database and defines the types of each field in the documents that will be stored in the index. We will define a mapping as a Python dictionary:mapping = { "mappings": { "properties": { "group": { "type": "text" }, "name": { "type": "text" }, "rating": { "type": "text" }, "n_rater": { "type": "text" }, "n_reviewer": { "type": "text" }, "summary": { "type": "text", "analyzer": "english" }, "process": { "type": "text" }, "ingredient": { "type": "text", }, "ml.tokens": { "type": "rank_features" } } } } Here, all fields are defined as text, which means they are full-text searchable. We also specify that the summary field should be analyzed using the English analyzer, which will help to optimize searches in English text by taking into account things such as stemming and stop words. Finally, we create the field that ELSER will use to create the token set, which is the result of the expansion happening based on the terms passed to ELSER.Creating the indexOnce we’ve defined our mapping, we can create the index in Elasticsearch with the following:es.indices.create(index=index_name, body=mapping)This sends a request to Elasticsearch to create an index with the specified name and mapping.Reading the CSV fileWith our index ready, we can now read our dataset from the CSV file. We’ll use pandas, a powerful data manipulation library in Python, to do this:import pandas as pd with open('recipe_dataset.csv', 'r', encoding='utf-8', errors='ignore') as file: df = pd.read_csv(file)This code opens the CSV file and reads it into a pandas dataframe, a two-dimensional tabular data structure that’s perfect for manipulating structured data.Converting to dictionariesTo index the data into Elasticsearch, we need to convert our dataframe into a list of dictionaries, where each dictionary corresponds to a row (i.e., a document or recipe) in the dataframe:recipes = df.to_dict('records') print(f"Number of documents: {len(recipes)}")At this point, we have our dataset ready to index in Elasticsearch. However, considering the size of the dataset, it is advisable to use the bulk indexing feature for efficient data ingestion. This will be covered in the next section.Bulk indexing the dataLet’s look into the step-by-step process of bulk indexing your dataset in Elasticsearch.Defining the preprocessing pipelineBefore we proceed to bulk indexing, we need to set up a pipeline to preprocess the documents. Here, we will use the elser-v1-recipes pipeline, which utilizes the ELSER model for semantic indexing. The pipeline is defined as follows:[ { "inference": { "model_id": ".elser_model_1", "target_field": "ml", "field_map": { "ingredient": "text_field" }, "inference_config": { "text_expansion": { "results_field": "tokens" } } } } ]The pipeline includes an inference processor that uses the ELSER pre-trained model to perform semantic indexing. It maps the ingredient field from the recipe data to the text_field object of the ELSER model. The output (the expanded tokens from the ELSER model) is stored in the tokens field under the ml field in the document.Creating a bulk indexing sequenceGiven the size of the Allrecipes.com dataset, it’s impractical to index each document individually. Instead, we can utilize Elasticsearch’s bulk API, which allows us to index multiple documents in a single request. First, we need to generate a list of dictionaries, where each dictionary corresponds to a bulk index operation:bulk_index_body = [] for index, recipe in enumerate(recipes): document = { "_index": "recipes", "pipeline": "elser-v1-recipes", "_source": recipe } bulk_index_body.append(document)In this loop, we iterate over each recipe (a dictionary) in our recipes list and then construct a new dictionary with the necessary information for the bulk index operation. This dictionary specifies the name of the index where the document will be stored (recipes), the ingest pipeline to be used to process the document (elser-v1-recipes), and the document source itself (recipe).Performing the bulk index operationWith our bulk_index_body array ready, we can now perform the bulk index operation:try: response = helpers.bulk(es, bulk_index_body, chunk_size=500, request_timeout=60*3) print ("\nRESPONSE:", response) except BulkIndexError as e: for error in e.errors: print(f"Document ID: {error['index']['_id']}") print(f"Error Type: {error['index']['error']['type']}") print(f"Error Reason: {error['index']['error']['reason']}")We use the helpers.bulk() function from the Elasticsearch library to provide our Elasticsearch connection (es)—the bulk_index_body array we just created—with a chunk_size value of 500 (which specifies that we want to send 500 documents per request) and a request_timeout value of 180 seconds, which specifies that we want to allow each request to take up to 3 minutes before timing out because the indexing could take a long time with ELSER.The helpers.bulk() function will return a response indicating the number of operations attempted and the number of errors, if any.If any errors occur during the bulk index operation, these will be raised as BulkIndexError. We can catch this exception and iterate over its errors attribute to get information about each individual error, including the ID of the document that caused the error, the type of error, and the reason for it.At the end of this process, you will have successfully indexed your entire Allrecipes.com dataset in Elasticsearch, ready for it to be retrieved and processed by your RAG-enhanced CookBot.ConclusionIn closing, the infusion of RAG, ELSER, and RRF technologies into CookBot elevates culinary exploration. With Elasticsearch indexing and the Allrecipes.com dataset, CookBot transcends traditional kitchen boundaries, offering personalized, context-aware assistance. This journey signifies the convergence of cutting-edge AI and the rich tapestry of culinary possibilities. As CookBot orchestrates flavor symphonies, the future of cooking is redefined, promising a delightful harmony of technology and gastronomy for every user. Embrace the evolution—where CookBot's intelligence transforms mere ingredients into a canvas for culinary innovation.Author BioBahaaldine Azarmi, Global VP Customer Engineering at Elastic, guides companies as they leverage data architecture, distributed systems, machine learning, and generative AI. He leads the customer engineering team, focusing on cloud consumption, and is passionate about sharing knowledge to build and inspire a community skilled in AI.Jeff Vestal has a rich background spanning over a decade in financial trading firms and extensive experience with Elasticsearch. He offers a unique blend of operational acumen, engineering skills, and machine learning expertise. As a Principal Customer Enterprise Architect, he excels at crafting innovative solutions, leveraging Elasticsearch's advanced search capabilities, machine learning features, and generative AI integrations, adeptly guiding users to transform complex data challenges into actionable insights.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!👋 Hello ,“There is not going to be one model to rule them all. You need to be trying out different models, you need a real choice of model providers.”  -Adam Selipsky, CEO, AWS. There’s no one-size-fits-all approach in AI development. When you embrace diversity in AI, that’s when it truly shines. There’s also a different side to the coin — the infinitely scalable adaptability of AI to revolutionize field after field, such as when it can help discover promising new sustainable battery materials to potentially reduce reliance on Lithium. Welcome back to a new issue of AI Distilled - your one-stop destination for all things AI, ML, NLP, and Gen AI. Let’s get started with the latest news and developments across different industries and sectors: AI Launches & Industry Updates: Explore the GPT MarketplaceNVIDIA Unveils Innovations in Gaming, AI, and Robotics at CES 2024 Perplexity AI Secures $73.6M Funding Led by NVIDIA and Jeff Bezos OpenAI Set to Launch GPT Store for AI Models and Apps Google Faces Multibillion-Dollar Patent Trial Over AI Technology in U.S. Google's DeepMind Unveils Advances in Robotic Training with Video and Language Models AI in Healthcare: Isomorphic Labs Secures $3 Billion AI-Driven Drug Discovery Deals with Eli Lilly and Novartis Nabla Secures $24 Million in Series B Funding for AI-Powered Medical Assistant AI in Business: Deloitte Introduces PairD AI Chatbot for 75,000 Staff in Big Four's Latest Automation Move Walmart Revolutionizes Shopping with Generative AI Innovations AI in Science & Technology: Microsoft and PNNL Harness AI to Discover Promising Battery Material German Automakers Pioneer AI Integration in Cars, Elevating Driving Experience AI in Finance: Rising Concerns as Generative AI Use Grows in Finance, Amplifying Misinformation Risks AI in Supply Chain Management: Warehousing Industry Leverages Machine Learning to Tackle Disruptions We’ve also curated the latest GPT and LLM resources, tutorials, and secret knowledge: Explore the Future of AI: A Guide to the Top 9 AI APIs of 2024 Optimizing LLM Inference with Splitwise: Achieving Efficiency in GPU Usage A Comprehensive Guide to Merging LLMs AI Drift in Retrieval Augmented Generation Finally, don’t forget to check-out our hands-on tips and strategies from the AI community for you to use on your own projects: Creating Your Own AI Image Generator App with Generative AI Optimizing Code Output with CodeWhisperer Mastering Knowledge Graph Construction with KeyBERT, HDBSCAN, and Zephyr-7B-Beta How to Craft an Open Source Multi-Modal RAG System Looking for some inspiration? Here are some GitHub repositories to get your projects going!gxnu-zhonglab/odtrack DLYuanGod/TinyGPT-V intel/intel-extension-for-transformers CambioML/pykoi  📥 Feedback on the Weekly EditionTake our weekly survey and get a free PDF copy of our best-selling book, "Interactive Data Visualization with Python - Second Edition." We appreciate your input and hope you enjoy the book!  Share your thoughts and opinions here! Writer’s Credit: Special shout-out to Vidhu Jain for their valuable contribution to this week’s newsletter content!  Cheers,  Merlyn Shelley  Editor-in-Chief, Packt  SignUp | Advertise | Archives⚡ TechWave: AI/GPT News & AnalysisAI Launches & Industry Updates: ⭐ Explore the GPT Marketplace: Just two months in, 3 million custom ChatGPTs are already out there! The GPT Store is now open to ChatGPT Plus, Team, and Enterprise users, offering a variety of handy GPTs. Get in on the action at chat.openai.com/gpts! ⭐ NVIDIA Unveils Innovations in Gaming, AI, and Robotics at CES 2024: NVIDIA unveiled impressive CES 2024 innovations: GeForce RTX 40 SUPER GPUs, AI laptops, generative AI tools. They highlighted RTX GPUs' influence on generative AI, introduced TensorRT acceleration for Stable Diffusion XL and SDXL Turbo, and NVIDIA Avatar Cloud Engine (ACE) Microservices for digital avatars. Getty Images and Nvidia introduced Generative AI by iStock, a text-to-image platform for customized stock photos. ⭐ Perplexity AI Secures $73.6M Funding Led by NVIDIA and Jeff Bezos: San Francisco's Perplexity AI secures $73.6 million in funding led by IVP, with Nvidia and Jeff Bezos participating, valuing the company at $520 million. Despite serving 500 million queries in 2023, profitability remains elusive, as it competes with Google in the search market. The funds will be used for hiring and product development. ⭐ OpenAI Set to Launch GPT Store for AI Models and Apps: OpenAI is set to launch the GPT Store, where developers can present custom GPT model applications, following updated policies. The launch, previously delayed, offers diverse, code-free applications. Revenue-sharing details await clarification. ⭐ Google Faces Multibillion-Dollar Patent Trial Over AI Technology in U.S.: Google is facing a federal jury trial in Boston as Singular Computing alleges patent infringement in its AI processors. Singular seeks up to $7 billion in damages, while Google argues independent development. The trial may last two to three weeks. ⭐ Google's DeepMind Unveils Advances in Robotic Training with Video and Language Models: DeepMind Robotics unveils AutoRT, a system enhancing robot understanding of human intentions using Visual Language Models. It orchestrates 20 robots, suggesting tasks via LLMs and introduces RT-Trajectory with 63% success in 41 tasks using video input. AI in Healthcare: ⭐ Isomorphic Labs Secures $3 Billion AI-Driven Drug Discovery Deals with Eli Lilly and Novartis: London-based Isomorphic, a DeepMind spin-out, forms strategic alliances with Eli Lilly and Novartis, valued at $3 billion. Utilizing AlphaFold 2 AI technology, Isomorphic focuses on accurate protein predictions for innovative drug discovery. ⭐ Nabla Secures $24 Million in Series B Funding for AI-Powered Medical Assistant: Paris startup Nabla secures $24 million in a Series B funding round led by Cathay Innovation and ZEBOX Ventures. Nabla develops an AI copilot for doctors, streamlining administrative tasks while collaborating with physicians. AI in Business: ⭐ Deloitte Introduces PairD AI Chatbot for 75,000 Staff in Big Four's Latest Automation Move: Deloitte is using a chatbot called PairD to help 75,000 employees in Europe and the Middle East with everyday tasks. While it's convenient, there are concerns about its accuracy, so employees still check its work. Deloitte is also sharing PairD with 800 workers at the charity Scope as part of its AI strategy. ⭐ Walmart Revolutionizes Shopping with Generative AI Innovations: Walmart introduces generative AI-powered features on iOS, Android, and its website to improve the digital shopping experience. These features provide personalized responses and recommendations, shifting from scrolling to goal-oriented searching for a smoother shopping journey. AI in Science & Technology: ⭐ Microsoft and PNNL Harness AI to Discover Promising Battery Material: Microsoft and PNNL used AI and cloud computing to speed up battery innovation, identifying a safer, efficient solid-state electrolyte with less lithium. Azure Quantum Elements platform screened 32 million candidates in 80 hours, highlighting a material with potential for a 70% reduction in sodium use, advancing sustainable energy solutions. ⭐ German Automakers Pioneer AI Integration in Cars, Elevating Driving Experience: Leading German automakers like Volkswagen and Mercedes-Benz are revolutionizing the automotive industry with advanced AI integration. Volkswagen unveiled ChatGPT technology, enhancing the driving experience with AI-powered chatbots and IDA voice assistants, while Mercedes-Benz introduced a sophisticated virtual assistant for context-based suggestions, marking a significant leap in interactive AI utilization at CES 2024. AI in Finance: ⭐ Rising Concerns as Generative AI Use Grows in Finance, Amplifying Misinformation Risks: The finance sector's growing use of generative AI is transforming services but raises concerns of misinformation. A study by PYMNTS Intelligence and AI-ID shows 80% of consumers worry about generative AI's misinformation risk. Regulatory guidelines, model explainability tools, and industry cooperation are essential for responsible AI adoption in finance. AI in Supply Chain Management: ⭐ Warehousing Industry Leverages Machine Learning to Tackle Disruptions: Zebra Technologies Corporation's research highlights the warehousing industry's adoption of AI, particularly machine learning (ML), amid challenges like inflation and labor shortages. The report predicts ML, predictive analytics, and mobile dimensioning will dominate by 2028, aiding historical analysis, demand prediction, and automation. Decision-makers aim to boost resilience with 94% planning ML integration within five years.  🔮 Expert Insights from Packt Community The Handbook of NLP with Gensim - By Chris Kuo Gensim and its NLP modeling techniques Gensim is actively maintained and supported by a community of developers and is widely used in academic research and industry applications. It covers many important NLP techniques that make up the workforce of today’s NLP. Last year, I was at a company’s year-end party. The ballroom was filled with people standing in groups with their drinks. I walked around and listened for conversation topics where I could chime in. I heard one group talking about the FIFA World Cup 2022 and another group talking about stock markets. I joined the stock markets conversation. In that short moment, my mind had performed “word extractions,” “text summarization,” and “topic classifications.” These tasks are the core tasks of NLP and what Gensim is designed to do. We perform serious text analyses in professional fields including legal, medical, and business. We organize similar documents into topics. Such work also demands “word extractions,” “text summarization,” and “topic classifications.” In the following sections, I will give you a brief introduction to the key models that Gensim offers so you will have a good overview. These models include the following: BoW and TF-IDF Latent semantic analysis/indexing (LSA/LSI) Word2Vec Doc2Vec Text summarization LDA Ensemble LDA  BoW and TF-IDF Texts can be represented as a bag of words, which is the count frequency of a word. BoW uses the word count to reflect the significance of a word. However, this is not very intuitive. Frequent words may not carry special meanings depending on the type of document. LSA/LSI Latent semantic analysis (LSA) was developed in the 1990s. It's an NLP solution that far surpasses naïve keyword matching and has become an important search engine algorithm. Prior to that, in 1988, an LSA-based information retrieval system was patented (US Patent #4839853, now expired) and named “latent semantic indexing,” so the technique is also called latent semantic indexing (LSI). Gensim and many other reports name LSA as LSI so as not to confuse LSA with LDA. This is an excerpt from the book The Handbook of NLP with Gensim - By Chris Kuo and published in OCT ‘23. To see what's inside the book, read the entire chapter here or try a 7-day free trial to access the full Packt digital library. To discover more, click the button below.      Read through the Chapter 1 unlocked here...  🌟 Secret Knowledge: AI/LLM Resources⭐ Explore the Future of AI: A Guide to the Top 9 AI APIs of 2024: In this guide, you'll learn how to navigate the dynamic realm of AI APIs, uncovering the capabilities of the top 9 for 2024. Discover Google Cloud Vision AI, an unparalleled eye for accurate image analysis, IBM Watson Assistant, a conversational genius transforming virtual assistance, Amazon Lex, empowering apps with voice commands effortlessly, Azure Cognitive Services, the Swiss Army knife of AI, offering diverse tools, DeepAI, simplifying deep learning for innovation, and decode texts with MonkeyLearn, a text analysis guru, among others. Read the post to explore how these APIs can shape your tech ventures and redefine the future of AI. ⭐ Optimizing LLM Inference with Splitwise: Achieving Efficiency in GPU Usage: Discover how Splitwise, a technique from Azure Research - Systems, boosts LLM inference efficiency. It separates prompt computation and token-generation phases, optimizing hardware use. This method enhances GPU cluster design, achieving higher throughput, lower costs, and reduced power for efficient LLM deployment. ⭐ A Comprehensive Guide to Merging LLMs: This comprehensive guide explores merging LLMs using the mergekit library without requiring a GPU. It covers four merging techniques: SLERP, TIES, DARE, and passthrough, with configuration examples. The result is Marcoro14–7B-slerp, a high-performing model featured on the Open LLM Leaderboard. ⭐ AI Drift in Retrieval Augmented Generation (RAG): This guide delves into AI drift within RAG pipelines, drawing from a real case where a customer faced declining AI responses. It covers the causes (content drift, LLM drift, pipeline algorithm changes) and strategies (content management, API upgrades, internal metrics) to control AI drift.  🔛 Masterclass: AI/LLM Tutorials⭐ Creating Your Own AI Image Generator App with Generative AI: Discover how to build a powerful Generative AI Text-to-Image application in this detailed guide. The author shares their journey of seamlessly integrating AI-generated images into a React app, using third-party APIs like SegMind. With a step-by-step walkthrough, you'll explore the code behind the app on GitHub and learn how to choose the right API, integrate it into React, and unleash AI capabilities in web development. Read on to bring dynamic, AI-generated content to your React projects and stay at the forefront of web development innovation. ⭐ Optimizing Code Output with CodeWhisperer: Unlock the full potential of Amazon CodeWhisperer with this in-depth guide on prompt engineering. Learn how CodeWhisperer accelerates software development by offering code recommendations based on natural language comments. The post provides step-by-step insights on effective prompt engineering in Python, emphasizing best practices such as crafting specific and concise prompts, incorporating additional context, utilizing multiple comments strategically, and understanding CodeWhisperer's capacity for cross-file context. ⭐ Mastering Knowledge Graph Construction with KeyBERT, HDBSCAN, and Zephyr-7B-Beta: Discover how to leverage LLMs with traditional NLP and ML methods to create knowledge graphs from unstructured text. The author showcases the synergy of KeyBERT, HDBSCAN, and Zephyr-7B-Beta for improved keyword extraction, clustering, and refinement. The guide covers dataset prep, keyword extraction, and LLM integration. ⭐ How to Craft an Open Source Multi-Modal RAG System: Discover building a Retrieval-Augmented Generation (RAG) system with an Open Source Large Language Multi-Modal (LLMM). Learn the integration of ChromeDB and Hugging Face, covering Clip, data storage, and MLLMs for user chat sessions in a detailed, dependency-free guide.  🚀 HackHub: Trending AI Tools⭐ gxnu-zhonglab/odtrack: Efficient video-level tracking pipeline utilizing online token propagation to densely capture contextual relationships and spatio-temporal trajectories across frames.  ⭐ DLYuanGod/TinyGPT-V: Features an efficient Multimodal Large Language Model using small backbones for efficiently incorporating multimodal capabilities into language models. ⭐ intel/intel-extension-for-transformers: Toolkit to accelerate GenAI/LLM performance on Intel platforms, including Gaudi2, CPU, and GPU, seamlessly compressing Transformer-based models, accessing optimized model packages, and using NeuralChat. ⭐ CambioML/pykoi: An open-source Python library for LLMs, enhancing them with RLHF, collecting user feedback, fine-tuning with reinforcement learning, comparing models, and creating RAG chatbots efficiently.  

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionAI and data analysis are critical for businesses to gain insights, optimize processes, and deliver value to their customers. However, working with data can be challenging, especially when it comes from various sources, formats, and systems. That’s why Microsoft offers two powerful platforms that can help you simplify your data and analytics needs: Microsoft Dataverse and Microsoft Fabric.Microsoft Dataverse is a smart, secure, and scalable low-code data platform that lets you create and run applications, flows, and intelligent agents with common tables, extended attributes, and semantic meanings. Microsoft Fabric is a unified platform that can meet your organization’s data and analytics needs. It integrates data lake, data engineering, and data integration from Power BI, Azure Synapse, and Azure Data Factory into a single SaaS experience enabling a single layer for managing data across the enterprise as well as enabling data scientists to apply models using data from these sources effectively.In this article, we will learn how to consume data from dataverse, prepare our data for ML experiments using other data sources, apply a machine learning model, and finally create an outcome that can be again by users of dataverse.Business ScenarioIn this blog, we will try to solve problems for retail merchants selling products such as wine, fruits , meat , fish, etc through web and in-store experience. the marketing department generates monthly sales offers but also wants to ensure these offers land to customers based on the revenue they generate throughout the year. While it is easy for existing customers with long-term relations, but many new customers would remain outside of such business segmentation for 12 months which makes it harder for marketing to engage. So we will help the marketing team to get predicted revenue available for all customers so they can plan the offers and realize the true potential with each customer.Getting the environment readyLet's understand where would be our data and how we can prepare for the use case. The marketing team in this case uses Microsoft dynamics Marketing solution . so customer information is stored in dataverse and in a table called “Contact”. The transaction information is stored in an e-commerce solution hosted in Azure and using Azure SQL as data storage. To replicate the environment we need to perform the following actions1. Download and deploy the “contact table solution” in the targeted dataverse environment. follow the steps defined at Microsoft Learn. The solution will deploy changes to the core contact table and a power automation flow that will be required to populate the data.2. Download “DataFile_Master” and place the file in one drive for business in the same environment as dataverse.3. Open power to automate flow “ArticleFlow_loadCustomerData” deployed through the solution file and update the file location at the following action and save.Figure 1 - List action from Power Automate Cloud Flow4. Run the flow so data required for simulation is uploaded in the contact table. After the execution of the flow, you should have the following data rows added to the contact table.Figure 2 - Data Created in Contact Table of Dataverse5. Let's set up a link between dataverse and Microsoft Fabric. This can be initiated by launching the Power App maker view and clicking on Analyze> Link to Microsoft Fabric link. Please refer to the documentation if you are performing this action for the first time. This will allow access to dataverse tables in Microsoft Fabric for all data science activities without any ETL needs.6. The last step of setting up the environment is to bring the transaction summary information to our Fabric. Here we will import the information in the Microsoft Fabric workspace created with dataverse synchronization. First, we will launch Lakehouse from workspace , and choose the one created with dataverse synchronization. The name would start from dataverse_<environment> where the environment is the name of the environment of dataverse.Figure 3- Microsoft Fabric Auto created Workspace and Lakehouse for dataverse7. This will bring you to the Lakehouse Explorer view where you can view all the dataverse tables. Now from the menu select “Get Data > Upload files” to bring our transaction summary file to Lakehouse. In a real scenario , this can also be done through many features offered by data factory  Figure 4 - Action to upload transaction file in Lakehouse Explorer8. Download a ready file “RetailStoreTxnSummary_01” and select the file in a prompt. This will upload the file under the file folder of Lakehouse.9. Click on three dots with the file and choose load to table > New table. This will show a prompt and ask for the table name , let's keep it the same as the file name. this will create a table with summary information.Figure 5 - Action to convert transaction summary file to Delta tableWe have completed the steps of data preparation and started preparing for the machine learning experiment. With the completion of the above action, our lakehouse now has a contact table, mirrored from dataverse and retailstoretxnsummary_01table created from CSV file. We can now move to the next step of preparing data for the machine learning model.Preparing data for machine learning modelAs discussed in our business problem, we will be using the data to create a linear regression model. Our intention is to use the customer information and transactional information to predict expected revenue from customers in a 12-month cycle.  Now that we have our data available in fabric, we can use Notebook at Fabric to create a joint table that will be used as a source of testing and training the model.  we will use the following code snippet to join the two tables and save them in the temp test_train_data table.  You can download the ready notebook file “01-Createtable” and upload using Open Notebook > Existing Notebook or create a new one from Open Notebook > New Notebook.Figure 6 - a snapshot of the notebook "01-CreateTable"Creating and registering ML modelIn this step we will now use the data in our table “tbl_temp_traintestdata” and build a linear regression model.  we are using linear regression as it is simple and easy to learn as well as suitable for numerical predictions. We will use the following PySpark code, you can also download the ready file “02-CreateMLMode”.Analyse the model performanceLet’s quickly assess the model performance using 3 evaluation criteria. firstly we use RMSE (Root Mean Squared Error), the training data is approximately 9535.31, and for the test data, it’s approximately 8678.23. The lower the RMSE, the better the model’s predictions. It’s worth noting that the RMSE is sensitive to outliers, meaning that a few large errors can significantly increase the RMSE. Second, we used R2 (R-squared). the R2 for the training data is approximately 0.78 (or 78%), and for the test data, it’s approximately 0.82 (or 82%). This means that about 78% of the variability in the training data and 82% of the variability in the test data can be explained by the model. This model seems to be performing reasonably well, as indicated by the relatively high R2 values. However, the RMSE values suggest that there may be some large errors, possibly due to outliers in the data. Lastly, we analyzed the coefficients to identify the importance of the selected features , the results were the following:Figure 7 - Coefficients of featuresWe identified that MntDairyProds (42.06%)  has the highest coefficient, meaning it has the most significant influence on the model’s predictions. A change in this feature will have a substantial impact on the predicted crffa_revenue. However, MntBeverageProds (0.05%) and others under 0 indicate they have less influence on the model’s predictions. However, they still contribute to the model’s ability to accurately predict crffa_revenue.Since data cleansing is not in our blog scope, we will accept these results and move to the next step which is using this model to perform batch prediction on new customer profiles.Using the model for batch predictionWe will now use the registered model to batch-predict new customer profiles. We will fetch the rows with the current revenue is zero and pass the dataset for prediction. Finally, we will save the records so we can post back to dataverse. A ready file “03– UseModelandSavePredictions” can be downloaded or the following code snippet can be copied.After execution of this code, we will have a new table in oneLake “” with predicted revenue. This table can be then synchronized back to dataverse using virtual table configuration and used in the table.Figure 8 - Output snapshot from OneLake SQL analytics endpointThere are detailed instructions are available at Microsoft Learn that can make the prediction table available in dataverse so the marketing team can use the predicted values for segmentation or personalized offeringsFigure 9 virtual table configured in Dataverse.ConclusionIn this blog, we have learned how to use Microsoft Fabric to create a regression model for predicting customer revenue and leveraging data from Dataverse and other sources. We have also seen how to consume the model through simple PySpark code. Moreover, we have explored how to integrate the model with Power Platform, opening up many new possibilities.This is just one example of how this platform can accelerate the work together to enable business scenarios that traditionally would have required data scientist and many days of engineering. There are many more possibilities and benefits of using these tools, such as scalability, security, governance, and collaboration. We encourage you to experiment and explore both dataverse and fabric to discover new ways of solving your business problems and creating value for your customers.Author BioMohammad Adeel Khan is a Senior Technical Specialist at Microsoft. A seasoned professional with over 19 years of experience with various technologies and digital transformation projects. At work , he engages with enterprise customers across geographies and helps them accelerate digital transformation using Microsoft Business Applications , Data, and AI solutions. In his spare time, he collaborates with like-minded and helps solve business problems for Nonprofit organizations using technology.  Adeel is also known for his unique approach to learning and development. During the COVID-19 lockdown, he introduced his 10-year-old twins to Microsoft Learn. The twins not only developed their first Microsoft Power Platform app—an expense tracker—but also became one of the youngest twins to earn the Microsoft Power Platform certification. 

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionIn an era dominated by instantaneous digital communication, businesses are constantly on the hunt for ways to refine and amplify their sales operations. Chatbots and platforms like ChatGPT stand at the forefront of this revolution, offering a blend of speed, accuracy, and personalized service. As companies look to the horizon, it becomes evident that AI-driven tools aren't just an option; they're a necessity. In this guide, we'll navigate through the transformative impacts of chatbots and ChatGPT on sales and offer a blueprint for leveraging their capabilities to their fullest potential. Let’s have a look at the advantages of implementing a chatbot as part of your sales channel1. Lead Generation Made EasyHow it works: Chatbots can be positioned on your website, social media pages, or even within apps. They initiate conversations, engage potential customers, and capture essential contact details.Benefits:Higher Engagement: Visitors are more likely to interact with an interactive bot than fill out a static form.Immediate Interaction: Chatbots can engage visitors instantly, increasing the likelihood of capturing leads.2. Efficient Lead QualificationHow it works: Instead of manual surveys or questionnaires, chatbots can quickly determine the potential of leads by asking relevant questions.Benefits:Time-saving: Automated lead qualification reduces manual efforts.Accuracy: Chatbots can consistently gather data and categorize leads based on predefined criteria.3. Instant Customer Queries Resolution with ChatGPTHow it works: ChatGPT uses advanced AI algorithms to comprehend user queries and provide precise answers.Benefits:Fast Responses: Instant answers mean no waiting time for customers.Consistency: AI ensures uniformity in responses, maintaining brand messaging.4. Personalized Product RecommendationsHow it works: ChatGPT can analyze past customer interactions, purchase histories, and preferences to suggest products or services.Benefits:Increased Sales: Personalized recommendations often resonate better with customers, leading to increased purchases.Enhanced Customer Experience: Customers appreciate tailored suggestions, enhancing their overall interaction with your brand.5. Elevating Customer ServiceHow it works: ChatGPT can address a wide range of customer concerns, from basic FAQs to complex product inquiries.Benefits:24/7 Support: AI doesn't sleep, ensuring round-the-clock customer assistance.Improved Satisfaction: Quick and accurate responses lead to happier, more loyal customers.6. Automation and Workload ReductionHow it works: Beyond answering questions, ChatGPT can manage routine tasks like booking appointments, sending reminders, or following up on leads.Benefits:Focus on Strategy: Sales teams can redirect their attention to more significant, high-value tasks.Consistent Follow-ups: Automated reminders and follow-ups ensure no potential lead is overlooked.7. Time-saving and Sales AugmentationHow it works: By automating repetitive tasks and offering intelligent recommendations, ChatGPT streamlines the sales process.Benefits:Faster Deal Closures: Efficient processes lead to quicker sales.Increased Revenue: More sales in less time amplify revenue generation.The fusion of chatbots and AI-driven platforms like ChatGPT offers an unprecedented advantage in the sales domain. By integrating these technologies into your sales strategy, you not only optimize operations but also provide an enhanced customer experience, setting the stage for sustainable business growth. Embrace the future of sales, and watch your business thrive!Let’s look at how to create a sales channel framework for chatbot using ChatGPTStep 1: Setting Up for Lead GenerationObjective: Engage visitors and gather essential contact details.Actions:Integrate the chatbot on prominent areas of your website, social media pages, and apps.Design conversation starters to engage users, such as: "Hello! How can I assist you today?"Prompt for ChatGPT: "Hello [User's Name], let's start by defining your sales objectives. Think about your main goals in sales. Is it to generate more leads? Maybe you want to qualify them more efficiently? Or perhaps you're looking to elevate your customer service game?Please list down all your sales objectives. Once you have them listed, we'll work together to prioritize them based on your immediate business requirements.Remember, the clearer your objectives, the more effective your tech solution deployment will be!"This prompt provides a clear directive and offers a little guidance, making it easier for the user to start listing their objectives.Step 2: Implement Efficient Lead QualificationObjective: Determine the potential of leads without manual surveys.Actions:Predefine criteria for categorizing leads (e.g., high potential, low interest).Create a set of relevant questions to gauge the user's interest and intent.Prompt: "Welcome, user! For a tailored experience with our SaaS collaboration tools, please answer a few questions. This automated process saves time, ensures accuracy, and helps us better understand your needs. From team size to current tools, your responses will guide our recommendations. Let's begin!"Step 3: Incorporate Instant Customer Query Resolution with AIObjective: Provide immediate and consistent answers.Actions:Train your chatbot using AI platforms like ChatGPT on frequently asked questions and typical user concerns.Continuously update the bot based on new questions and feedback to improve precision.Prompt: "Hello [User's Name], I'm here to assist you with your questions or concerns. Please provide a detailed description of your query or issue, and I'll do my best to give you a precise and accurate answer. Remember, the more specific you are, the better I can assist. Let's get started!"Step 4: Personalized Product RecommendationsObjective: Enhance user experience by suggesting tailored products/services.Actions:Integrate the chatbot with your CRM to analyze past customer interactions and purchase histories.Design the bot to recommend products based on user preferences and past behavior.Prompt: “Hello [User's Name], based on your history with us, I'd like to suggest fitting products or services. Can you mention the last item you engaged with? Also, are you seeking an upgrade, complementary items, or exploring new areas? Any specific preferences for the recommendation?"Step 5: Elevate Customer ServiceObjective: Provide round-the-clock support and address a wide range of concerns.Actions:Implement a 24/7 availability feature.Train the bot on both basic FAQs and in-depth product inquiries.Prompt: “Hello [User's Name], I'm here to assist you with any questions or concerns you may have about our products or services. From basic FAQs to in-depth product inquiries, I've got you covered.Please detail your query, and I'll provide a quick and accurate response. Remember, I'm available 24/7, so don't hesitate to reach out at any time. Your satisfaction is our priority. Let's get your questions answered!"ConclusionThe evolution of sales operations hinges on the symbiotic relationship between human expertise and AI efficiency. As evidenced in this guide, chatbots, especially powerhouse models like ChatGPT, aren't just transforming sales—they're redefining the entire customer journey. By integrating these technologies, businesses don't just streamline operations; they usher in a new era of customer interaction, marked by personalized experiences, swift resolutions, and enhanced satisfaction. The future of sales is here, and it beckons businesses to adapt, innovate, and thrive. Embracing these tools is no longer a luxury; it's an imperative for sustainable growth in a digital-first world.Author BioDr. Anshul Saxena is an author, corporate consultant, inventor, and educator who assists clients in finding financial solutions using quantum computing and generative AI. He has filed over three Indian patents and has been granted an Australian Innovation Patent. Anshul is the author of two best-selling books in the realm of HR Analytics and Quantum Computing (Packt Publications). He has been instrumental in setting up new-age specializations like decision sciences and business analytics in multiple business schools across India. Currently, he is working as Assistant Professor and Coordinator – Center for Emerging Business Technologies at CHRIST (Deemed to be University), Pune Lavasa Campus. Dr. Anshul has also worked with reputed companies like IBM as a curriculum designer and trainer and has been instrumental in training 1000+ academicians and working professionals from universities and corporate houses like UPES, CRMIT, and NITTE Mangalore, Vishwakarma University, Pune & Kaziranga University, and KPMG, IBM, Altran, TCS, Metro CASH & Carry, HPCL & IOC. With a work experience of 5 years in the domain of financial risk analytics with TCS and Northern Trust, Dr. Anshul has guided master's students in creating projects on emerging business technologies, which have resulted in 8+ Scopus-indexed papers. Dr. Anshul holds a PhD in Applied AI (Management), an MBA in Finance, and a BSc in Chemistry. He possesses multiple certificates in the field of Generative AI and Quantum Computing from organizations like SAS, IBM, IISC, Harvard, and BIMTECH.Author of the book: Financial Modeling Using Quantum Computing

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!👋 Hello ,🎉 Joyous 2024! Wishing you a year as delightful as your dreams!  Dive into the new year with our outstanding edition, filled with essential features to boost your AI practice. “The speed at which people will be able to come up with an idea, to test the idea, to make something, it’s going to be so accelerated…You don’t need to have a degree in computer science to do that.” - Matthew Candy, IBM’s global managing partner for generative AI Coding without coding is a revolutionary idea indeed. What might have been previously perceived as unbelievable is a living reality and new features like Github’s Copilot Chat make it all the more seamless.  The real possibilities of AI expand far beyond computing, with the technology making waves in healthcare, finance, and supply chain management. Starting from this edition, we’ll bring you fresh updates from each of these sectors, so stay tuned! Let's kick things off by tapping into the latest news and developments. AI Launches & Industry Updates:  Microsoft Copilot Integrates with GenAI Music App Suno for Song Composition Google Plans Potential Layoffs Amidst AI Integration in Ad Sales GitHub Expands Copilot Chat Availability for Developers AI in Healthcare: AI Streamlining Health Insurance Shopping Process Revolutionizing Healthcare with AI Stethoscope on Smartphones Generative AI's Impact on Mental Health Counseling AI in Finance: Next-Gen Banks to Leverage AI for Financial Influence and Support Invest Qatar Introduces Cutting-Edge Azure OpenAI GPT-Powered Chatbot AI in Supply Chain Management: AI Safeguards Supply Chains Amidst Holiday Challenges AI-SaaS Integration Revolutionizes E-commerce Analytics Here are some handpicked GPT and LLM resources, tutorials, and secret knowledge that’ll come in handy for your next project: Understanding the Prompt Development Life Cycle Building Platforms with LLMs: Overcoming Challenges in Summarization as a Service Understanding the Risks of Prompt Injection in LLM Applications Creating an Open Source LLM Recommender System: Mastering Prompt Iteration and Optimization Looking for hands-on tips and strategies straight from the developer community? We’ve got you covered: Exploring Google's Gemini Pro Vision LLM with Javascript: A Practical Guide Accelerating AI Application Productionization: A Guide with SageMaker JumpStart, Amazon Bedrock, and TruEra Quantizing LLMs with Activation-aware Weight Quantization (AWQ) Unlocking Your MacBook's AI Potential: Running 70B LLM Models Without Quantization Check out our curated list of smoking hot GitHub repositories: Giskard-AI/giskard CopilotKit/CopilotKit chengzeyi/stable-fast ml-explore/mlx  📥 Feedback on the Weekly EditionQ: How can we foster effective collaboration between humans and AI systems, ensuring that AI complements human skills and enhances productivity without causing job displacement or widening societal gaps?Share your valued opinions discreetly! Your insights could shine in our next issue for the 39K-strong AI community. Join the conversation! 🗨️✨ As a big thanks, get our bestselling "Interactive Data Visualization with Python - Second Edition" in PDF. Let's make AI_Distilled even more awesome! 🚀 Jump on in! Share your thoughts and opinions here! Writer’s Credit: Special shout-out to Vidhu Jain for their valuable contribution to this week’s newsletter content!  Cheers,  Merlyn Shelley  Editor-in-Chief, Packt  SignUp | Advertise | Archives⚡ TechWave: AI/GPT News & AnalysisNew Launches & Industry Updates: ⭐ Microsoft Copilot Integrates with GenAI Music App Suno for Song Composition: Microsoft Copilot has partnered with GenAI music app Suno, enabling users to create complete songs including lyrics, instrumentals, and singing voices. Accessible via Microsoft Edge, the integration aims to make music creation inclusive and enjoyable. However, ethical and legal concerns persist, with some artists uncomfortable with AI algorithms learning from their work without consent or compensation. Suno attempts to address such issues by blocking certain prompts and preventing the generation of covers using existing lyrics. Read Microsoft’s official blog here. ⭐ Google Plans Potential Layoffs Amidst AI Integration in Ad Sales: Google is reportedly considering laying off around 30,000 employees within its ad sales division due to the implementation of internal AI, aiming for improved operational efficiency. The restructuring primarily targets the ad sales team, reflecting Google's exploration of AI benefits in operational processes. Earlier in 2023, Google had already laid off 12,000 employees, emphasizing the need for organizational adaptation amidst evolving global dynamics. Read about other significant 2023 layoffs here. ⭐ GitHub Expands Copilot Chat Availability for Developers: GitHub is extending the availability of Copilot Chat, a programming-centric chatbot powered by GPT-4, to all users. The tool was initially launched for Copilot for Business subscribers and later in beta for $10 per month users. Integrated into Microsoft's IDEs, Visual Studio Code and Visual Studio, it's included in GitHub Copilot's paid tiers and free for verified teachers, students, and maintainers of specific open-source projects. Developers can prompt Copilot Chat in natural language, seeking real-time guidance on code-related tasks. Know more about Copilot Chat here. AI in Healthcare: ⭐ AI Streamlining Health Insurance Shopping Process: Companies are utilizing AI to simplify the often complex and tedious task of shopping for health insurance, aiming to guide consumers to better and more affordable options. With many Americans sticking to their health plans due to the difficulty of predicting their future healthcare needs, AI-powered tools gather individual information and predict the most suitable health plans. Alight, a cloud-based HR services provider, reports that 95% of its served employers use AI technology, including a virtual assistant, for employee health benefits selection.  ⭐ Revolutionizing Healthcare with AI Stethoscope on Smartphones: A startup AI Health Highway is addressing the challenge of limited access to specialists in healthcare by introducing an innovative solution, AI Steth, which combines traditional stethoscope use with cutting-edge signal processing and AI. Targeting the early detection and prediction of heart and lung disorders, the device transforms sound patterns into visual representations on smartphones, allowing non-specialists like family physicians and nurses to examine patients effectively. AI Steth has shown exceptional accuracy in murmur detection, paving the way for more objective and efficient diagnoses. Discover AI Health Highway’s work here. ⭐ Generative AI's Impact on Mental Health Counseling: Generative AI is finding use in mental health counseling, sparking discussions about its potential to assist or even replace human therapists. Recent research testing ChatGPT on mental health counseling questions has raised questions about the technology's role in therapy. AI therapy has evolved from basic chatbots to sophisticated entities capable of nuanced emotional responses, offering accessible mental health support 24/7. While the benefits are evident, challenges such as risk, coverage, and ethical considerations must be addressed for responsible implementation.  AI in Finance: ⭐ Next-Gen Banks to Leverage AI for Financial Influence and Support: Experts predict that next-generation banks will harness generative AI to impact various aspects of financial services, ranging from influencing customer decisions to identifying vulnerable clients. Tom Merry, Head of Banking Strategy at Accenture, suggests that generative AI could significantly influence banking operations, touching nearly every aspect. While the UK banking industry has been utilizing AI for fraud detection and risk analysis, the introduction of generative AI, capable of creating novel solutions based on extensive data, is gaining traction. ⭐ Invest Qatar Introduces Cutting-Edge Azure OpenAI GPT-Powered Chatbot: Invest Qatar, in collaboration with Microsoft, has launched Ai.SHA, an innovative AI-powered chatbot utilizing GPT capabilities through the Azure OpenAI service. This move positions Invest Qatar as a pioneer among investment promotion agencies globally, embracing advanced technology to transform interactions between investors and businesses in Qatar. Ai.SHA acts as a comprehensive resource, providing information on business opportunities, the investment ecosystem, and business setup in Qatar.  AI in Supply Chain Management: ⭐ AI Safeguards Supply Chains Amidst Holiday Challenges: Businesses face unique challenges in managing complex supply chains amid the holiday season, from counterfeit airplane parts to recalls affecting festive foods. The reliance on suppliers underscores the need for transparency and visibility to prevent disruptions caused by supplier misconduct. Leveraging AI in contracts offers a solution, allowing businesses to streamline due diligence, enhance visibility, conduct predictive analytics, and align with environmental, social, and governance (ESG) regulations. AI-powered contracts emerge as vital tools to proactively address supply chain challenges and ensure customer trust during the holiday season and beyond. ⭐ AI-SaaS Integration Revolutionizes E-commerce Analytics: In the logistics sector, where precision and speed are critical, SaaS coupled with AI is transforming traditional approaches. This integration allows for real-time data processing and learning from it, offering unprecedented insights and optimization capabilities. Learn how AI-SaaS integration streamlines inventory, boosts operational efficiency, and fortifies against fraud, becoming the recipe for e-commerce success in a hypercompetitive landscape.  🔮 Expert Insights from Packt Community Architectural Patterns and Techniques for Developing IoT Solutions - By Jasbir Singh Dhaliwal Unique requirements of IoT use cases IoT use cases tend to have very unique requirements concerning power consumption, bandwidth, analytics, and more. Additionally, the inherent complexity of IoT implementations (computationally challenged field devices on one end of the spectrum vis-à-vis almost infinite capacity of the cloud on the other) forces architects to make difficult architectural decisions and implementation choices. Before presenting the various IoT patterns, it is worth mentioning the unique expectations from IoT architectures that are different from non-IoT architectures: Sensing events and actuation commands have a wide range of latency expectations – from real-time to fire and forget. Data analysis results need to be reported/visualized/consumed on a variety of consumer devices – mobiles, desktops, tablets, and more. Similarly, data consumers have diverse backgrounds, data needs, and application roles (personas). One is often forced to integrate with legacy as well as cutting-edge devices and/or external systems – very few trivial use cases have isolated/standalone architectures. There is a considerable difference in the way the data is extracted from legacy versus non-legacy systems – legacy systems may internally collate the data and then push it to the external port (file transfer), whereas newer systems may push the data in a continuous stream (time-series data). This variability is one of the critical considerations when choosing a particular IoT architectural pattern. Varied deployment requirements – edge, on-premise, hybrid, the cloud, and more. Adherence to strict regulatory compliances, especially in medical and aeronautical domains. There are expectations considering immediate payback, return on investment (ROI), business outcomes, and new service business models. Continuous innovation, which results in new services or offerings (especially by cloud vendors), forcing IoT architectures to be in continuous sync mode with these new offerings or services. This is an excerpt from the book Architectural Patterns and Techniques for Developing IoT Solutions written by Jasbir Singh Dhaliwal and published in Sep ‘23. To see what's inside the book, read the entire chapter here or try a 7-day free trial to access the full Packt digital library. To discover more, click the button below.   Read through the Chapter 1 unlocked here...  🌟 Secret Knowledge: AI/LLM Resources⭐ Understanding the Prompt Development Life Cycle: Explore PDLC and gain insights into how prompt engineering mirrors software development. The primer unfolds a step-by-step guide, beginning with the Initial Build phase where an imperfect prompt is crafted, incorporating techniques like zero-shot and few-shot. The Optimization stage strategically refines prompts based on historical data. Finally, the Fine-tune phase demonstrates the refinement of models, emphasizing the importance of continuous tracking. ⭐ Building Platforms with LLMs: Overcoming Challenges in Summarization as a Service: Get to know more about Summarization as a Service, a platform designed by a Microsoft team for Viva Engage. Learn about the complexities of prompt design, ensuring accuracy and grounding, addressing privacy and compliance concerns, managing performance, cost, and availability of LLM services, and integrating outputs seamlessly with the Copilot and other Viva Engage features.  ⭐ Understanding the Risks of Prompt Injection in LLM Applications: Explore the intricacies of prompt injection in LLM applications. The author emphasizes the critical security implications and potential impacts, citing the OWASP Top 10 for LLM Applications. Drawing parallels to injection vulnerabilities like A03 in traditional security, the article illustrates potential risks through a thought experiment involving a robotic server.  ⭐ Creating an Open Source LLM Recommender System: Mastering Prompt Iteration and Optimization: Open Recommender is an open-source YouTube video recommendation system adept at tailoring content to your interests based on Twitter feed analysis. Discover its data pipeline, utilizing GPT-4, and the transition towards cost-effective open-source models using OpenPipe. Explore the challenges faced during prompt iteration, with a focus on better prompt engineering tools, including the introduction of a TypeScript library, Prompt Iteration Assistant.    🔛 Masterclass: AI/LLM Tutorials⭐ Exploring Google's Gemini Pro Vision LLM with Javascript: A Practical Guide: The blog introduces the concept of multi-modal LLMs capable of interpreting various data modes, including images. Learn how to utilize Google's multi-modal Gemini Pro Vision LLM with Javascript. The tutorial guides you through creating an AI-powered nutrition-fact explainer app using the newly released LLM. The tutorial covers prerequisites, such as installing node.js and obtaining a Gemini LLM API key.  ⭐ Accelerating AI Application Productionization: A Guide with SageMaker JumpStart, Amazon Bedrock, and TruEra: The post emphasizes the importance of observability in LLM applications and provides insights into evaluating responses for honesty, harmlessness, and helpfulness. You'll learn how to deploy, fine-tune, and iterate on foundation models for LLM applications using Amazon SageMaker JumpStart, Amazon Bedrock, and TruEra.  ⭐ Quantizing LLMs with Activation-aware Weight Quantization (AWQ): Explore the application of Activation-aware Weight Quantization (AWQ) to democratize LLMs like Llama-2, making them more accessible for deployment on regular CPUs or less powerful GPUs. The process involves setting up a GPU instance, installing necessary packages like AutoAWQ and transformers, and saving the quantized model. The tutorial further covers the model upload to the Hugging Face Model Hub and concludes with the successful reduction of the Llama-2 model from ~27GB to ~4GB, enhancing its efficiency for diverse applications. ⭐ Unlocking Your MacBook's AI Potential: Running 70B LLM Models Without Quantization: Discover how to unleash the hidden AI power of your 8GB MacBook as this post explores the latest 2.8 version of AirLLM. Without the need for quantization or model compression, an ordinary MacBook can now efficiently run top-tier 70 billion parameter models. Explore the MacBook's AI capabilities, understanding Apple's role in AI evolution through its M1, M2, and M3 series GPUs, which offer competitive performance in the era of generative AI. Gain insights into GPU capabilities, memory advantages, and the open-source MLX platform. 🚀 HackHub: Trending AI Tools⭐ Giskard-AI/giskard: Specialized testing framework for ML models, covering a range from tabular to LLMs. Developers can efficiently scan AI models using just four lines of code. ⭐ CopilotKit/CopilotKit: Build in-app AI chatbots that seamlessly interact with the app state, execute actions within the app, and communicate with both frontend, backend, and third-party services via plugins, serving as an AI "second brain" for users. ⭐ chengzeyi/stable-fast:  Leverage stable-fast for efficient and high-performance inference on various diffuser models while enjoying fast model compilation and out-of-the-box support for dynamic shape, LoRA, and ControlNet. ⭐ ml-explore/mlx: Array framework for machine learning on Apple silicon by Apple's ML research, offering familiar Python and C++ APIs closely aligned with NumPy and PyTorch. 

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!This article is an excerpt from the book, The Future of Finance with ChatGPT and Power BI, by James Bryant, Alok Mukherjee. Enhance decision-making, transform your market approach, and find investment opportunities by exploring AI, finance, and data visualization with ChatGPT's analytics and Power BI's visuals.IntroductionLLMs, such as OpenAI’s GPT series, can sometimes generate responses that are referred to as “hallucinations.” These are instances where the output from the model is factually incorrect, it presents information that it could not possibly know (given it doesn’t have access to real-time or personalized data), or it might output something nonsensical or highly improbable.Let’s explore deeper into what hallucinations are, how to identify them, and what steps can be taken to mitigate their impact, especially in a context where accurate and reliable information is crucial, such as financial analysis, trading, or visual data presentations.Understanding hallucinationsLet’s look at some examples:Factual inaccuracies: Suppose an LLM provides information stating that Apple Inc. was founded in 1985. This is a clear factual inaccuracy because Apple was founded in 1976.Speculative statements: If an LLM were to suggest that “As of 2023, Tesla’s share price has hit $3,000,” this is a hallucination. The model doesn’t know real-time data and any post-2021 prediction or speculation it makes about specific stock prices is unfounded.Confident misinformation: For instance, if an LLM confidently states that “Amazon has declared bankruptcy in late 2022,” this is a hallucination and can have serious consequences if it’s acted upon without verification.How can we spot hallucinations?Here are some useful ways to spot hallucinations:Cross-verification: If an LLM suggests an unusual trading strategy, such as shorting a typically stable blue-chip stock based on some supposed insider information, always cross-verify this advice with other reliable sources or consult a financial advisor.Questioning the source: If an LLM claims that “our internal data shows a bullish trend for cryptocurrency X,” this is likely a hallucination. The model doesn’t have access to proprietary internal data.Time awareness: If the model provides information or trends post-September 2021 without the user explicitly asking for a hypothetical or simulated scenario, consider this a red flag. For example, GPT-4 giving specific “real-time” market cap values for companies in 2023 would be a hallucination.What can we do about hallucinations?Here are some ideas:Promote awareness: If you are developing an AI-assisted trading app that uses an LLM, ensure users are aware of potential hallucinations, perhaps with a disclaimer or notification upon usageImplement checks: You might integrate a news API that could help validate major financial events or claims made by the modelMinimizing hallucinations in the futureThere are various ways we can minimize hallucinations. Here are some examples:Training improvements: Imagine developing a better model that understands context and sticks to the known data more closely, avoiding speculative or incorrect financial statements. Future versions of the model could be specifically trained on financial data, news, and reports to understand the context and semantics of financial trading and investment better. We could do this to ensure that it understands a short squeeze scenario accurately, or is aware that penny stocks typically come with higher risks.Better evaluation metrics: For instance, develop a specific metric that calculates the percentage of the model’s outputs that were flagged as hallucinations during testing. In the development phase, the models could be evaluated on more focused tasks such as generating valid trading strategies or predicting the impact of certain macroeconomic events on stock prices. The better the model performs on these tasks, the lower the chance of hallucinations occurring.Post-processing methods: Develop an algorithm that cross-references model outputs against reliable financial data sources and flags potential inaccuracies. After the model generates a potential trading strategy or investment suggestion, this output could be cross-verified using a rules-based system. For instance, if the model suggests shorting a stock that has consistently performed well without any recent negative news or poor earnings reports, the system might flag this as a potential hallucination.As an example, you can use libraries such as yfinance or pandas_datareader to access real-time or historical financial data:!pip install yfinance pandas_datareader import yfinance as yf def get_stock_data(ticker, start, end): stock = yf.Ticker(ticker) data = stock.history(start=start, end=end) return data # Example Usage: data = get_stock_data("AAPL", "2021-01-01", "2023-01-01")You could also develop a cross-verification algorithm and compare the model’s outputs with the collected financial data to flag potential inaccuracies.Integration with real-time data: While creating Power BI visualizations, data that’s been pulled from the LLM could be cross-verified with real-time data from financial databases or APIs. Any discrepancies, such as inconsistent market share percentages or revenue growth rates, could be flagged. This reduces the risk of presenting hallucinated data in visualizations. Let’s look at some examples: Extracting real-time data: You can continue to use yfinance or pandas_datareader to extract real-time data Cross-verifying with real-time data: You can compare the model’s output with real-time data to identify discrepancies:def real_time_cross_verify(output, real_time_data): # Assume output is a dict with keys 'market_share', 'revenue_ growth', and 'ticker' ticker = output['ticker'] # Fetch real-time data (assuming a function get_real_time_ data is defined) real_time_data = get_real_time_ data(ticker) # Compare the model's output with real-time data if abs(output['market_share'] - real_time_data['market_ share']) > 0.05 or \ abs(output['revenue_growth'] - real_time_data['revenue_ growth']) > 0.05: return True # Flagged as a potential hallucination return False # Not flagged # Example Usage: output = {'market_share': 0.25, 'revenue_growth': 0.08, 'ticker': 'AAPL'} real_time_data = {'market_share': 0.24, 'revenue_growth': 0.07, 'ticker': 'AAPL'} flagged = real_time_cross_verify(output, real_time_data)User feedback loop: A mechanism can be incorporated to allow users to report potential hallucinations. For instance, if a user spots an error in the LLM’s output during a Power BI data analysis session, they can report this. Over time, these reports can be used to further train the model and reduce hallucinations.OpenAI is on the caseTo tackle the chatbot’s missteps, OpenAI engineers are working on ways for its AI models to reward themselves for outputting correct data when moving toward an answer, instead of rewarding themselves only at the point of conclusion. The system could lead to better outcomes as it incorporates more of a human-like chain-of-thought procedure, according to the engineers.These examples should help in illustrating the concept and risks of LLM hallucinations, particularly in high-stakes contexts such as finance. As always, these models should be seen as powerful tools for assistance, but not as a final authority.Trading examplesHallucination scenario: Let’s assume you’ve asked an LLM for a prediction on the future performance of a specific stock, let’s say Tesla. The LLM might generate a response that appears confident and factual, such as “Based on the latest earnings report, Tesla has declared bankruptcy.” If you acted on this hallucinated information, you might rush to sell Tesla shares only to find out that Tesla is not bankrupt at all. This is an example of a potentially disastrous hallucination.Action: Before making any trading decision based on the LLM’s output, always cross-verify the information from a reliable financial news source or the company’s official communications.Power BI visualization examplesHallucination scenario: Suppose you’re using an LLM to generate text descriptions for a Power BI dashboard that tracks the market share of different automakers in the EV market. The LLM might hallucinate and produce a statement such as “Rivian has surpassed Tesla in terms of global EV market share.” This statement might be completely inaccurate as Tesla had a significantly larger market share than Rivian.Action: When using LLMs to generate text descriptions or insights for your Power BI dashboards, it’s crucial to cross-verify any assertions that are made by the model. You can do this by cross-referencing the underlying data in your Power BI dashboard or by referring to reliable external sources of information.To minimize hallucinations in the future, the model can be fine-tuned with a dataset that’s been specifically curated to cover the relevant domain. The use of a structured validation set can help spot and rectify hallucinations during the model training process. Also, employing a robust fact-checking mechanism on the output of the model before acting on its suggestions or insights can help catch and rectify any hallucinations.Remember, while LLMs can provide valuable insights and suggestions, their output should always be used as one of many inputs in your decision-making process, particularly in high-stakes environments such as financial trading and analysis.ConclusionIn the dynamic world of financial analysis and data visualization, the presence of LLM 'hallucinations' poses a challenge. Awareness, verification, and ongoing improvement strategies stand as pillars against these inaccuracies. While LLMs offer invaluable support, their outputs must be scrutinized, verified, and used as one among many tools in decision-making. As we navigate this landscape, vigilance, continuous refinement, and a critical eye will fortify our ability to harness the power of LLMs while mitigating the risks they present in high-stakes financial contexts.Author BioJames Bryant, a finance and technology expert, excels at identifying untapped opportunities and leveraging cutting-edge tools to optimize financial processes. With expertise in finance automation, risk management, investments, trading, and banking, he's known for staying ahead of trends and driving innovation in the financial industry. James has built corporate treasuries like Salesforce and transformed companies like Stanford Health Care through digital innovation. He is passionate about sharing his knowledge and empowering others to excel in finance. Outside of work, James enjoys skiing with his family in Lake Tahoe, running half marathons, and exploring new destinations and culinary experiences with his wife and daughter.Aloke Mukherjee is a seasoned technologist with over a decade of experience in business architecture, digital transformation, and solutions architecture. He excels at applying data-driven solutions to real-world problems and has proficiency in data analytics and planning. Aloke worked at EMC Corp and Genentech and currently spearheads the digital transformation of Finance Business Intelligence at Stanford Health Care. In addition to his work, Aloke is a Certified Personal Trainer and is passionate about helping his clients stay fit. Aloke also has a passion for wine and exploring new vineyards. 

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!This article is an excerpt from the book, ChatGPT for Cybersecurity Cookbook, by Clint Bodungen. Master ChatGPT and the OpenAI API, and harness the power of cutting-edge generative AI and large language models to revolutionize the way you perform penetration testing, threat detection, and risk assessment.IntroductionWith the rise of cyber threats, organizations of all sizes are increasingly aware of the importance of training their staff on email phishing, a common and potentially dangerous tactic employed by cybercriminals. In this recipe, we'll be using ChatGPT to create a tool for interactive email phishing training. This recipe guides you through the process of crafting a specialized prompt to turn ChatGPT into a simulation tool for phishing attack awareness. With this approach, you can use ChatGPT to train users to identify potential phishing emails, thereby increasing their awareness and helping to protect your organization from potential security threats. What makes this truly powerful is its interactive nature. ChatGPT will present the user with a series of email scenarios. The user will then decide whether the email is a phishing attempt or a legitimate email, and can even ask for more details such as the URL to a link in the email or header information, for example. ChatGPT will provide feedback, ensuring a continuous, engaging, and efficient learning experience. Additionally, we will also cover how to use Python in conjunction with these prompts to create exportable email simulation scenarios. This feature can be beneficial in situations where you might want to use the generated scenarios outside of ChatGPT, such as in a live course or in a Learning Management System (LMS). Getting ready Before diving into this recipe, ensure you have your OpenAI account set up and your API key on hand. If not, you should refer back to Chapter 1 for the necessary setup details. You will also need Python version 3.10.x or later.  Additionally, confirm you have the following Python libraries installed: 1. openai: This library enables you to interact with the OpenAI API. Install it using the command pip install openai. 2. os: This is a built-in Python library, which allows you to interact with the operating system, especially for accessing environment variables. 3. tqdm: This library is utilized for showing progress bars during the policy generation process. Install it with pip install tqdm. How to do it… In this section, we will walk you through the process of creating an interactive email phishing training simulation using ChatGPT. The instructions are step-by-step, starting from logging into your OpenAI account and ending with generating phishing training simulations. 1. Access the ChatGPT interface. Log into your OpenAI account and go to the ChatGPT interface at https://chat.openai.com. 2.Initialize the simulation by entering the specialized prompt. The following prompt is carefully designed to instruct ChatGPT to act as a phishing training simulator. Enter the prompt into the text box and press Enter. "You are a cybersecurity professional and expert in adversarial social engineering tactics, techniques, and procedures, with 25 years of experience. Create an interactive email phishing training simulation (for employees). Provide no other response other than to ask the question, "Is the following email real or a phishing attempt? (You may ask clarification questions such as URL information, header information, etc.)" followed by simulated email, using markdown language formatting. The email you present can represent a legitimate email or a phishing attempt, which can use one or more various techniques. Provide no further generation or response until I answer the question. If I answer correctly, just respond with "Correct" and a short description to further explain the answer, and then restart the process from the beginning. If I answer incorrectly, respond with "Incorrect", then the correct answer, then a short description to further explain the answer. Then repeat the process from the beginning.    Present me with only 3 simulations in total throughout the process and remember my answer to them all. At least one of the simulations should simulate a real email. After the last question has been answered, and after your response, end the assessment and give me my total score, the areas I did well in and where I need to improve." Tip Be sure to change the number of simulations ChatGPT provides, to suit your needs. Now, ChatGPT will generate interactive email phishing scenarios based on your instructions. Respond to each scenario as if you were the employee undergoing the training. After the third scenario and your final response, ChatGPT will calculate and provide your total score, areas of strength, and areas for improvement. How it works… At the heart of this recipe lies the specialized prompt. This prompt is constructed to instruct ChatGPT to act as an interactive phishing training tool, delivering a series of email phishing scenarios. The prompt follows certain design principles which are essential to its effectiveness and interaction with the OpenAI models. Here, we'll dissect those principles: 1. Defining the role: The prompt starts by setting up the role of the AI model – a cybersecurity professional and expert in adversarial social engineering tactics, techniques, and procedures, with 25 years of experience. By defining the AI's persona, we direct the model to generate responses using the knowledge and expertise expected from such a role. 2. Detailed instructions and simulation: The instructions given in the prompt are meticulously detailed, and it is this precision that enables ChatGPT to create effective and realistic phishing simulations. The prompt asks the AI model to generate a phishing email scenario, followed by the question, "Is the following email real or a phishing attempt?". Notably, the AI model is given the liberty to provide additional clarifying questions, such as asking about URL information, header information, etc., giving it the freedom to generate more nuanced and complex scenarios. By asking the model to generate these emails using markdown language formatting, we ensure that the simulated emails have the structure and appearance of genuine emails, enhancing the realism of the simulation. The AI is also instructed to present emails that can represent either legitimate correspondence or a phishing attempt, ensuring a diverse range of scenarios for the user to evaluate. How can ChatGPT convincingly simulate phishing emails? Well, ChatGPT's strength comes from the wide variety of text it has been trained on, including (but not limited to) countless examples of email correspondences and probably some instances of phishing attempts or discussions around them. From this extensive training, the model has developed a robust understanding of the format, tone, and common phrases used in both legitimate and phishing emails. So, when prompted to simulate a phishing email, it can draw on this knowledge to generate a believable email that mirrors the features of a real-world phishing attempt. As the model doesn't generate responses until it receives an answer to its question, it guarantees an interactive user experience. Based on the user's response, the model provides relevant feedback ("Correct" or "Incorrect"), the correct answer if the user was wrong, and a brief explanation. This detailed, immediate feedback aids the learning process and helps to embed the knowledge gained from each simulated scenario. It's worth noting that, although the model has been trained to generate human-like text, it doesn't understand the content in the same way humans do. It doesn't have beliefs, opinions, or access to real-time, world-specific information or personal data unless explicitly provided in the conversation. Its responses are merely predictions based on its training data. The carefully designed prompt and structure are what guide the model to generate useful, contextually appropriate content for this particular task. 3. Feedback mechanism: The prompt instructs the AI to provide feedback based on the user's answer, further explaining the answer. This creates an iterative feedback loop that enhances the learning experience. 4. Keeping track of progress: The prompt instructs the AI to present three simulations in total and to remember the user's answer to all of them. This ensures continuity in the training and enables tracking of the user's progress. 5. Scoring and areas of improvement: After the final simulation and response, the prompt instructs the AI to end the assessment and provide a total score along with areas of strength and areas for improvement. This helps the user understand their proficiency and the areas they need to focus on for improvement. ChatGPT’s models are trained on a broad range of internet text. However, it's essential to note that it does not know specifics about which documents were part of its training set or have access to any private, confidential, or proprietary information. It generates responses to prompts by recognizing patterns and producing text that statistically aligns with the patterns it observed in its training data. By structuring our prompt in a way that clearly defines the interactive assessment context and expected behavior, we're able to leverage this pattern recognition to create a highly specialized interactive tool. The ability of the OpenAI models to handle such a complex and interactive use case demonstrates their powerful capability and flexibility. There’s more… If you're using a Learning Management System (LMS) or conducting a live class, you might prefer to have a list of scenarios and details rather than an interactive method like ChatGPT. In these settings, it's often more practical to provide learners with specific scenarios to ponder and discuss in a group setting. The list can also be used for assessments or training materials, offering a static reference point that learners can revisit as needed, or as content for a phishing simulation system. By modifying the script from the previous recipe, you can instruct the ChatGPT model to produce a set of phishing email simulations complete with all necessary details. The resulting text can be saved into a file for easy distribution and usage in your training environment. Since this script is so similar to the one from the previous recipe, we’ll just cover the modifications instead of steppping through the entire script again. Let's walk through the necessary modifications: 1.      Rename and modify the function: The function generate_question is renamed to generate_email_simulations, and its argument list and body are updated to reflect its new purpose. It will now generate the phishing email simulations instead of cybersecurity awareness questions. This is done by updating the messages that are passed to the OpenAI API within this function. def generate_email_simulations() -> str:    # Define the conversation messages    messages = [        {"role": "system", "content": 'You are a cybersecurity professional and expert in adversarial social engineering tactics, techniques, and procedures, with 25 years of experience.'},        {"role": "user", "content": 'Create a list of fictitious emails for an interactive email phishing training. The emails can represent a legitimate email or a phishing attempt, using one or more various techniques. After each email, provide the answer, contextual descriptions, and details for any other relevant information such as the URL for any links in the email, header information. Generate all necessary information in the email and supporting details. Present 3 simulations in total. At least one of the simulations should simulate a real email.'},    ]    ... Note You can adjust the number of scenarios here to suit your needs. In this example, we've requested 3 scenarios. 2. Remove unnecessary code: The script no longer reads content categories from an input file, as it's not required in your use case. 3. Update variable and function names: All variable and function names referring to "questions" or "assessment" have been renamed to refer to "email simulations" instead, to make the script more understandable in the context of its new purpose. 4. Call the appropriate function: The generate_email_simulations function is called instead of the generate_question function. This function initiates the process of generating the email simulations. # Generate the email simulations email_simulations = generate_email_simulations() Tip Like the previous method, more scenarios will require a model that supports a larger context window. However, the gpt-4 model seems to provide better results in terms of accuracy, depth, and consistency with its generations for this recipe. Here’s how the complete script should look: import openai import os import threading import time from datetime import datetime # Set up the OpenAI API openai.api_key = os.getenv("OPENAI_API_KEY") current_datetime = datetime.now().strftime('%Y-%m-%d_%H-%M-%S') assessment_name = f"Email_Simulations_{current_datetime}.txt" def generate_email_simulations() -> str:    # Define the conversation messages    messages = [        {"role": "system", "content": 'You are a cybersecurity professional and expert in adversarial social engineering tactics, techniques, and procedures, with 25 years of experience.'},        {"role": "user", "content": 'Create a list of fictitious emails for an interactive email phishing training. The emails can represent a legitimate email or a phishing attempt, using one or more various techniques. After each email, provide the answer, contextual descriptions, and details for any other relevant information such as the URL for any links in the email, header information. Generate all necessary information in the email and supporting details. Present 3 simulations in total. At least one of the simulations should simulate a real email.'},    ]    # Call the OpenAI API    response = openai.ChatCompletion.create(        model="gpt-3.5-turbo",        messages=messages,        max_tokens=2048,        n=1,        stop=None,        temperature=0.7,    )    # Return the generated text    return response['choices'][0]['message']['content'].strip() # Function to display elapsed time while waiting for the API call def display_elapsed_time():    start_time = time.time()    while not api_call_completed:        elapsed_time = time.time() - start_time        print(f"\rElapsed time: {elapsed_time:.2f} seconds", end="")        time.sleep(1) api_call_completed = False elapsed_time_thread = threading.Thread(target=display_elapsed_time) elapsed_time_thread.start() # Generate the report using the OpenAI API try:    # Generate the email simulations    email_simulations = generate_email_simulations() except Exception as e:    print(f"\nAn error occurred during the API call: {e}")    exit() api_call_completed = True elapsed_time_thread.join() # Save the email simulations into a text file try:    with open(assessment_name, 'w') as file:        file.write(email_simulations)    print("\nEmail simulations generated successfully!") except Exception as e:    print(f"\nAn error occurred during the email simulations generation: {e}") By running this modified script, the ChatGPT model is directed to generate a series of interactive email phishing training scenarios. The script then collects the generated scenarios, checks them for errors, and writes them to a text file. This gives you a ready-made training resource that you can distribute to your learners or incorporate into your LMS or live training sessions. ConclusionIn conclusion, leveraging ChatGPT for interactive email phishing training empowers users with immersive, realistic simulations, bolstering cybersecurity awareness and defense. This innovative approach fosters a proactive stance against threats, ensuring organizations stay ahead in the ever-evolving landscape of cyber risks. With adaptable learning and dynamic feedback, ChatGPT transforms education, creating a robust line of defense against potential security breaches.Author BioClint Bodungen is a cybersecurity professional with 25+ years of experience and the author of Hacking Exposed: Industrial Control Systems. He began his career in the United States Air Force and has since many of the world's largest energy companies and organizations, working for notable cybersecurity companies such as Symantec, Kaspersky Lab, and Booz Allen Hamilton. He has published multiple articles, technical papers, and training courses on cybersecurity and aims to revolutionize cybersecurity education using computer gaming (“gamification”) and AI technology. His flagship product, ThreatGEN® Red vs. Blue, is the world’s first online multiplayer cybersecurity simulation game, designed to teach real-world cybersecurity.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionArtificial intelligence is playing an increasingly crucial role in assisting businesses and organizations to process huge volumes of data that the world is producing. The development of huge language models to evaluate enormous amounts of text data is one of the largest challenges in AI research. Databricks Dolly revolutionized the Databricks project, opening the door for more complex NLP models and improving the field of AI technology.Databricks Dolly for AIBefore we deep dive into Databricks Dolly and its impact on the future of AI adoption, let’s understand the basics of Large Language Models and their current challenges.Large Language Models & Databricks DollyAn artificial intelligence system called a large language model is used to produce human-like language and comprehend natural language processing activities. These models are created using deep learning methods and are trained on a lot of text input using a neural network design. Its major objective is to produce meaningful and coherent text from a given prompt or input. There are many uses for this, including speech recognition, chatbots, language translation, etc.They have gained significant popularity because of below capabilities :Text GenerationLanguage TranslationClassification and Categorization Conversational AIRecently ChapGPT from OpenAI, Google Bard, and Bing have created unified models for training and fine-tuning such models at a large scale. Now the issue with these LLMs is that they save user data on external servers, opening the cloud to unauthorized users and increasing the risk of sensitive data being compromised. Additionally, They may provide irrelevant information that could potentially injure users and lead to poor judgments, as well as offensive, discriminating content against certain individuals.In order to overcome this challenge, there is a need for open-source alternatives that promote the accuracy, and security of Large Language Models. The Databricks team has built Databricks Dolly, an open-source chatbot that adheres to these criteria and performs exceptionally in a variety of use situations, in response to these requirements after carefully examining user issues.Databricks Dolly can produce text by responding to questions, summarising ideas, and other natural language commands. It is built on an open-source, 6-billion-parameter model from EleutherAI that has been modified using the databricks-dolly-15k dataset of user-generated instructions. Due to Dolly's open-source nature and commercial licensing, anyone can use it to build interactive applications without having to pay for API access or divulge their data to outside parties. Dolly may be trained for less than $30, making construction costs low. Data can be saved in the DBFS root or another cloud object storage location that we specify when Dolly generates an answer. Using Dolly, we can design, construct, and personalize LLM without sharing any data.                                                         Image 1 - Databricks Dolly DifferentiatorsDemocratizing the magic of Databricks DollyWith Databricks Dolly , we can manage the below types of engagements.1.  Open & Close ended Question and Answers2.  Information Parsing from web3.  Detailed Answers based on the input4.  Creative Writing Now, Let’s see in detail how we can use Databricks dolly.Step 1 : Install Required LibrariesUse the below command in Databricks notebook or use cmd to install the required packages.%pip install "accelerate>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2"                                                                                   Image 2 - Databricks Dolly Package InstallationAs you can see from the image, once we execute this command in Databricks, the required packages are installed.Accelerate : Accelerate the training of machine learning modelsTransformers : Collection of pre-trained models for NLP activitiesTorch : To build and train deep learning modelsStep 2 : Input to the Databricks DollyOnce the model is loaded, the next step is to generate text based on the generate_next function.                                                                                                                     Image 3 - Databricks Dolly - Create Pipeline for remote code executionHere, the pipeline function from the Transformers library is used to execute the NLP tasks such as text generation, sentiment analysis, etc. Option trust_remote_code is used for the remote code execution.Step 3 : Pipeline reference to parse the output                                                                   Image 4 -Databricks Dolly - Create a Pipeline for remote code executionNow, the final step is to provide the textual input to the model using the generate_text function to which will use the language model to generate the response.Best Practices of Using Databricks DollyBe specific and lucid in your instructions to DollyUse Databricks Machine Learning Models to train and deploy Dolly for a scalable and faster executionUse the hugging face library and repo which has multiple tutorials and examplesConclusionThis article describes the difficulties that organizations have adopting Large Language Models and how Databricks may overcome these difficulties by utilising Dolly. Dolly gives businesses the ability to create a customized LLM that meets their unique requirements and has the added benefit of having open-source source code. In order to maximize LLM performance, the article also highlights the significance of recommended practices.Author Bio:Sagar Lad is a Cloud Data Solution Architect with a leading organization and has deep expertise in designing and building Enterprise-grade Intelligent Azure Data and Analytics Solutions. He is a published author, content writer, Microsoft Certified Trainer, and C# Corner MVP.Link - Medium , Amazon , LinkedIn

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!This article is an excerpt from the book, ChatGPT for Cybersecurity Cookbook, by Clint Bodungen. Master ChatGPT and the OpenAI API, and harness the power of cutting-edge generative AI and large language models to revolutionize the way you perform penetration testing, threat detection, and risk assessment.IntroductionIn this recipe, you'll learn how to harness the power of ChatGPT and the OpenAI API to create comprehensive vulnerability assessment plans using network, system, and business details as input. This recipe is invaluable for both cybersecurity students and beginners looking to familiarize themselves with proper methods and tools for vulnerability assessments, as well as experienced cybersecurity professionals aiming to save time on planning and documentation. Building upon the skills acquired in Chapter 1, you will delve deeper into establishing the system role of a cybersecurity professional specializing in vulnerability assessments. You'll learn how to craft effective prompts that generate well-formatted output using markdown language. This recipe will also expand on the techniques explored in Enhancing Output with Templates (Application: Threat Report) and Formatting Output as a Table (Application: Security Controls Table) in Chapter 1, enabling you to design prompts that produce the desired output format. Finally, you'll discover how to use the OpenAI API and Python to generate the vulnerability assessment plan, and then export it as a Microsoft Word file. This recipe will serve as a practical guide for creating detailed and efficient vulnerability assessment plans using ChatGPT and the OpenAI API. Getting Ready Before diving into the recipe, you should already have your OpenAI account setup and have obtained your API key. If not, revisit Chapter 1 for details. You will also need to be sure you have the following Python libraries installed: 1. python-docx: This library will be used to generate Microsoft Word files. You can install it using the command pip install python-docx.  2. tqdm: This library will be used to display progress bars. You can install it using the command:pip install tqdmHow to do it… In this section, we will walk you through the process of using ChatGPT to create a comprehensive vulnerability assessment plan tailored to a specific network and organization's needs. By providing the necessary details and using the given system role and prompt, you will be able to generate a well-structured assessment plan. 1. Begin by logging in to your ChatGPT account and navigating to the ChatGPT web UI. 2. Start a new conversation with ChatGPT by clicking the "New chat" button. 3.  Enter the following prompt to establish a system role: You are a cybersecurity professional specializing in vulnerability assessment. 4. Enter the following message text, but replace the placeholders in the “{ }” brackets with the appropriate data of your choice. You can either combine this prompt with the system role or enter it separately as follows: Using cybersecurity industry standards and best practices, create a complete and detailed assessment plan (not a penetration test) that includes: Introduction, outline of the process/methodology, tools needed, and a very detailed multi-layered outline of the steps. Provide a thorough and descriptive introduction and as much detail and description as possible throughout the plan. The plan should not only assessment of technical vulnerabilities on systems but also policies, procedures, and compliance. It should include the use of scanning tools as well as configuration review, staff interviews, and site walk-around. All recommendations should following industry standard best practices and methods. The plan should be a minimum of 1500 words. Create the plan so that it is specific for the following details: Network Size: {Large} Number of Nodes: {1000} Type of Devices: {Desktops, Laptops, Printers, Routers} Specific systems or devices that need to be excluded from the assessment: {None} Operating Systems: {Windows 10, MacOS, Linux} Network Topology: {Star} Access Controls: {Role-based access control} Previous Security Incidents: {3 incidents in the last year} Compliance Requirements: {HIPAA} Business Critical Assets: {Financial data, Personal health information} Data Classification: {Highly confidential} Goals and objectives of the vulnerability assessment: {To identify and prioritize potential vulnerabilities in the network and provide recommendations for remediation and risk mitigation.} Timeline for the vulnerability assessment: {4 weeks{ Team: {3 cybersecurity professionals, including a vulnerability assessment lead and two security analysts} Expected deliverables of the assessment: {A detailed report outlining the results of the vulnerability assessment, including identified vulnerabilities, their criticality, potential impact on the network, and recommendations for remediation and risk mitigation.} Audience: {The organization's IT department, senior management, and any external auditors or regulators.} Provide the plan using the following format and markdown language: #Vulnerability Assessment Plan ##Introduction Thorough Introduction to the plan including the scope, reasons for doing it, goals and objectives, and summary of the plan ##Process/Methodology Description and Outline of the process/Methodology ##Tools Required List of required tools and applications, with their descriptions and reasons needed ##Assessment Steps Detailed, multi-layered outline of the assessment steps Hint If you are performing this in the OpenAI Playground, it is advisable to use Chat mode and enter the role in the System window, and the prompt in the User message window. Figure 2.1 shows the system role and user prompt entered into the OpenAI Playground.  Figure 2.1 – OpenAI Playground Method 5. Review the generated output from ChatGPT. If the output is satisfactory and meets the requirements, you can proceed to the next step. If not, you can either refine your prompt or re-run the conversation to generate a new output. 6. Once you have obtained the desired output, you can use the generated markdown to create a well-structured vulnerability assessment plan in your preferred text editor or markdown viewer.  Figure 2.2 shows an example of ChatGPT generation of a vulnerability assessment plan using markdown language formatting.  Figure 2.2 – Example ChatGPT Assessment Plan Output How it works… This GPT-assisted vulnerability assessment plan recipe leverages the sophistication of natural language processing (NLP) and machine learning algorithms to generate a comprehensive and detailed vulnerability assessment plan. By adopting a specific system role and an elaborate user request as a prompt, ChatGPT is able to customize its response to meet the requirements of a seasoned cybersecurity professional who is tasked with assessing an extensive network system. Here's a closer look at how this process works: 1. System Role and Detailed Prompt: The system role designates ChatGPT as a seasoned cybersecurity professional specializing in vulnerability assessment. The prompt, which serves as the user request, is detailed and outlines the specifics of the assessment plan, from the size of the network and types of devices to the required compliance and the expected deliverables. These inputs provide context and guide ChatGPT's response, ensuring it is tailored to the complexities and requirements of the vulnerability assessment task. 2. Natural Language Processing and Machine Learning: NLP and machine learning form the bedrock of ChatGPT's capabilities. It applies these technologies to understand the intricacies of the user request, learn from the patterns, and generate a well-structured vulnerability assessment plan that is detailed, specific, and actionable. 3. Knowledge and Language Understanding Capabilities: ChatGPT uses its extensive knowledge base and language understanding capabilities to conform to industry-standard methodologies and best practices. This is particularly important in the rapidly evolving field of cybersecurity, ensuring that the resulting vulnerability assessment plan is up-to-date and adheres to recognized standards. 4. Markdown Language Output: The use of markdown language output ensures that the plan is formatted in a consistent and easy-to-read manner. This format can be easily integrated into reports, presentations, and other formal documents, which is crucial when communicating the plan to IT departments, senior management, and external auditors or regulators. 5. Streamlining the Assessment Planning Process: The overall advantage of using this GPT-assisted vulnerability assessment plan recipe is that it streamlines the process of creating a comprehensive vulnerability assessment plan. You save time on planning and documentation and can generate a professional-grade assessment plan that aligns with industry standards and is tailored to the specific needs of your organization. By applying these detailed inputs, you transform ChatGPT into a potential tool that can assist in creating a comprehensive, tailored vulnerability assessment plan. This not only bolsters your cybersecurity efforts but also ensures your resources are utilized effectively in protecting your network systems. ConclusionIn harnessing ChatGPT and the OpenAI API, this guide unlocks a streamlined approach to crafting detailed vulnerability assessment plans. Whether a novice or seasoned cybersecurity professional, leveraging these tools optimizes planning and documentation. By tailoring assessments to specific network intricacies, it fosters precision in identifying potential threats and fortifying defenses. Embrace this method to not only save time but also ensure comprehensive security measures aligned with industry standards, safeguarding networks effectively.Author BioClint Bodungen is a cybersecurity professional with 25+ years of experience and the author of Hacking Exposed: Industrial Control Systems. He began his career in the United States Air Force and has since many of the world's largest energy companies and organizations, working for notable cybersecurity companies such as Symantec, Kaspersky Lab, and Booz Allen Hamilton. He has published multiple articles, technical papers, and training courses on cybersecurity and aims to revolutionize cybersecurity education using computer gaming (“gamification”) and AI technology. His flagship product, ThreatGEN® Red vs. Blue, is the world’s first online multiplayer cybersecurity simulation game, designed to teach real-world cybersecurity.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionRetrieval Augmented Generation, or RAG, is a method that can expand the breadth or depth of information for Large Language Models (LLM). Retrieving and delivering more data to an LLM will result in applications with more contextual, relevant information. Unlike traditional web or mobile applications designed to retrieve structured data, RAG (Retrieval-Augmented Generation) requires data to be structured and indexed, and stored differently, most commonly in a vector database. The resulting experience should provide more contextual information with the added ability to cite the source of information. This narrower scope of information can result in a higher degree of accuracy and utility for your enterprise. To summarize RAG:Retrieval: When a question or formulated to an LLM-powered chat bot, RAG reviews the index to find relevant facts. This is like searching through an index where all the information is neatly summarized for quick access.Augmentation: It then takes these facts and feeds them to the language model, essentially giving it a brief on the subject matter at hand.Generation: With this briefing, the language model is now ready to craft a response that's not just based on what it already knows but also on the latest information it has just pulled in. In short, RAG keeps language models up-to-date and relevant, providing answers that are informed by the latest available data. It's a practical way to ensure AI remains accurate and useful for your organization, especially when dealing with current and evolving topics that may not be public knowledge. In this article, we will explore the differences between RAG and Fine tuning and how you can organize your RAG solution using Azure Machine Learning prompt flow. Retrieval Augmented Generation (RAG) vs Fine TuningWhen working with large language models through chat interfaces like ChatGPT, you will see that its foundational knowledge is point-in-time data. Fine-tuning, on the other hand, is akin to customizing the model with a new layer of knowledge that reflects your specific data, which becomes part of the model's intelligence. As of this article, OpenAI released GPT 4 Turbo which is based on a data set through April 2023. Extending an LLM’s body of knowledge can involve fine-tuning or RAG.Fine Tuning Foundational ModelsFine-tuning involves training a foundational model on a dataset specific to your application, effectively customizing the model to perform better for certain tasks or styles. To fine-tune a model, you need to have a dataset that represents the task or style you are aiming for and the computational resources to perform the training.Once fine-tuned, the model's knowledge is enhanced, and these changes are permanent unless the model is fine-tuned again with additional data. Fine-tuning is ideal for tasks needing deep customization where the information may be specialized but require re-training infrequently. While OpenAI has started to offer fine-tuning for certain models like GPT-3.5, not all foundational models or versions can be fine-tuned due to access restrictions to their parameters and training regimes.Retrieval Augmented GenerationRAG is like adding a live feed of information to the foundational model, enabling it to respond with the latest data without modifying the model itself. It involves augmenting a foundational language model’s response by dynamically integrating information retrieved from an external database, typically a vector database, at the time of the query.No Model Training Required: The foundational model's core parameters remain unchanged. Instead, RAG serves as a real-time data layer that the model queries to inform its responses.Real-time and Up to Date: Because RAG queries external data sources in real-time, it ensures that the language model's responses are enhanced by the most current and relevant information available.Image Credit: https://medium.com/@minh.hoque/retrieval-augmented-generation-grounding-ai-responses-in-factual-data-b7855c059322 RAG is widely adopted as the best starting point due to the following factors:Data Dynamics: Choose RAG for frequently changing data and fine-tuning for static, specialized domains. Like any data wrangling and model training problem, the results are only as good as your data quality.Resource Availability: With RAG you do not need expansive computational resources and budget like fine-tuning. You will still need skilled resources to implement and test RAG.Flexibility and Scalability: RAG offers adaptability to continuously add current information and ease of maintenance.Approaching RAG with Azure Machine Learning Prompt FlowWith a solid foundation of RAG vs fine-tuning, we will dive into the details of an approach to Retrieval Augmented Generation within Azure. Azure provides multiple solutions for creating and accessing vector indexes per Microsoft’s latest documentation. Azure offers 3 methods currently: Azure AI Studio, use a vector index and retrieval augmentation.Azure OpenAI Studio, use a search index with or without vectors.Azure Machine Learning, use a search index as a vector store in a prompt flow.Azure's approach to RAG lets you tailor the model to your business needs and integrate in private or public facing applications. What remains consistent is the ability to prepare and feed your data into the LLM of your choice. Within Azure Machine Learning Prompt Flow, Microsoft includes a number of practical features including a fact-checking layer alongside the existing model to ensure accuracy. Additionally, you can feed supplementary data directly to your large language models as prompts, enriching their responses with up-to-date and relevant information. Azure Machine Learning simplifies the process to augment your AI powered app with the latest data without the time and financial burdens often associated with comprehensive model retraining.  A benefit of using these services is the scalability and security and compliance functions that are native to Azure. A standard feature of Azure Machine Learning for ML models or LLMs is a point and click flow or notebook code interface to build your AI pipelines1.  Data Acquisition and Preparation with Azure Services for Immediate LLM Access:Azure Blob Storage for Data Storage is perfect for staging your data. These files can be anything from text files to PDFs.2. Vectorization and Indexing of your Data Using AI Studio and Azure AI SearchThis is a step that can be completed using one of multiple approaches including both open source and Azure native. Azure AI Studio significantly simplifies the creation and integration of a vector index for Retrieval-Augmented Generation (RAG) applications. Here are the main steps in the process:Initialization: Users start by selecting their data sources in Azure AI Studio, choosing from blob storage for easier testing and local file uploads.Index Creation: The platform guides users through configuring search settings and choosing an index storage location, with a focus on ease of use and minimal need for manual coding.This is one of many examples of how Azure AI Studio is democratizing the use of advanced RAG applications by merging and integrating different services in the Azure cloud together.                                                                                    SOURCE: Microsoft3. Constructing RAG Pipelines with Azure Machine Learning:Simplified RAG Pipeline Creation: With your index created, you can integrate it along with AI search as a plug-and-play component into your Prompt flow. With no/low code interface, you can drag and drop components to create your RAG pipeline.                                                                               Image Source: Microsoft.com Customization with Jupyter Notebooks: For those who are comfortable coding in Jupyter notebooks, Azure ML offers the flexibility to utilize Jupyter Notebooks natively. This will provide more control over the RAG pipeline to fit your project's unique needs. Additionally, there are other alternative flows that you can construct using libraries like LangChain as an alternative to using the Azure services.3. Manage AI Pipeline OperationsAzure Machine Learning provides a foundation designed for iterative and continuous updates. The full lifecycle for model deployment includes test data generation and prompt evaluation. ML and AI operations are needed to understand certain adjustments. For organizations already running Azure ML, prompt flow fits nicely into broader machine learning operations.Integrating RAG workflow into MLOPs pipeline through codes from azureml.core import Experiment from azureml.pipeline.core import Pipeline from azureml.pipeline.steps import PythonScriptStep # Create an Azure Machine Learning experiment experiment_name = 'rag_experiment' experiment = Experiment(ws, experiment_name) # Define a PythonScriptStep for RAG workflow integration rag_step = PythonScriptStep(name='RAG Step',                            script_name='rag_workflow.py',                            compute_target='your_compute_target',                            source_directory='your_source_directory',                            inputs=[rag_dataset.as_named_input('rag_data')],                            outputs=[],                            arguments=['--input_data', rag_dataset],                            allow_reuse=True) # Create an Azure Machine Learning pipeline with the RAG step rag_pipeline = Pipeline(workspace=ws, steps=[rag_step]) # Run the pipeline as an experiment pipeline_run = experiment.submit(rag_pipeline) pipeline_run.wait_for_completion(show_output=True) Here is the code snippets to create and manage data using Azure. from azureml.core import Dataset # Assuming you have a dataset named 'rag_dataset' in your Azure Machine Learning workspace rag_dataset = Dataset.get_by_name(ws, 'rag_dataset') # Split the dataset into training and testing sets train_data, test_data = rag_dataset.random_split(percentage=0.8, seed=42) # Convert the datasets to pandas DataFrames for easy manipulation train_df = train_data.to_pandas_dataframe() test_df = test_data.to_pandas_dataframe() ConclusionIt is important to note that the world of AI and LLMs is evolving at a rapid pace where months make a difference.  Azure Machine Learning for Retrieval Augmented Generation offers a transformative approach to leveraging Large Language Models and provides a compelling solution for enterprises that already have a competency center. Azure ML machine learning pipelines for data ingestion, robust training, management, and deployment capabilities for RAG is lowering the barrier for dynamic data integration with LLMs like OpenAI. As adoption continues to grow, we will see lots of exciting new use cases and success stories coming from organizations that adopt early and iterate fast. The benefit of Microsoft Azure is a single, managed and supported suite of services some of which already may be deployed within your organization. Azure services to support new AI adoption demands, Retrieval Augmented Generation included! Author BioRyan Goodman has dedicated 20 years to the business of data and analytics, working as a practitioner, executive, and entrepreneur. He recently founded DataTools Pro after 4 years at Reliant Funding, where he served as the VP of Analytics and BI. There, he implemented a modern data stack, utilized data sciences, integrated cloud analytics, and established a governance structure. Drawing from his experiences as a customer, Ryan is now collaborating with his team to develop rapid deployment industry solutions. These solutions utilize machine learning, LLMs, and modern data platforms to significantly reduce the time to value for data and analytics teams.