How-To Tutorials - ChatGPT

IntroductionThe emergence of AI technology, such as ChatGPT and Auto-GPT, has presented a wealth of possibilities for industries across the full spectrum of work. This article introduces ChatGPT and its use cases, followed by analyzing the shortcomings and challenges of ChatGPT integration. Overview of ChatGPT  A chatbot is an artificial intelligence-based computer program designed to simulate conversation with human users through a messaging interface. Chatbots can be a valuable tool to learn about AI and natural language processing, as well as to improve their communication skills. By interacting with a chatbot, beginners can practice their language skills, get feedback, and learn new vocabulary. Additionally, chatbots can provide instant answers to their questions, help them with simple tasks, and guide them through complex processes. For beginners, chatbots can be a useful tool in automating various tasks. Chatbots can also help in providing personalized recommendations, answering frequently asked questions, and offering support.   Technical Use Cases of ChatGPT  ChatGPT can be an excellent resource for experts and technical people in various fields. As a language model, it can provide answers to complex questions, aid in problem-solving, and assist in research. With its vast knowledge database and ability to understand and process natural language, ChatGPT can quickly and efficiently find relevant information and provide accurate answers. This can save time for experts and technical people, allowing them to focus on higher-level tasks that require their expertise. In addition to answering questions and providing information, ChatGPT can also be used by professionals for technical tasks. It can automate processes, such as data analysis, text classification, and language translation, making it an excellent tool for technical people. For example, a data scientist could use ChatGPT to automate the process of analyzing large data sets, while a developer could use it to quickly translate code or troubleshoot technical issues. With its versatility and adaptability, ChatGPT can be a valuable asset to technical people in various fields. Shortcomings and Challenges of ChatGPT  While ChatGPT is a highly advanced and impressive technology, there are still some shortcomings and challenges associated with it.  One of the main challenges is the potential for bias and inaccurate responses based on the data on which it was trained. As with any machine learning model, ChatGPT is only as good as the data it was trained on, so if the training data contains biases or inaccuracies, it may reproduce them in its responses.  Another challenge is the lack of transparency in its decision-making process, which can make it difficult to understand why it generates certain responses.  ChatGPT may struggle with context-dependent conversations and may not always supply accurate or helpful responses to complex or nuanced queries.  Response based on reinforcement learning could potentially be problematic for ChatGPT. Reinforcement learning involves the use of a reward system to incentivize the model to produce certain responses. However, if the feedback supplied is incorrect or biased, it can negatively affect the ChatGPT model's learning and lead to the production of suboptimal responses.  ChatGPT does not provide any confidence score for its response, for example, if an algorithm has multiple functions or sections, it does not provide a confidence score for each section. Hence, it raises questions about the reliability of its responses and how to measure that reliability.  ChatGPT, like any machine learning model, has limitations in its ability to predict the future. While it can generate responses based on patterns it has learned from enormous amounts of data, it cannot anticipate events that have not yet occurred or make predictions beyond its training data. The energy consumption and carbon footprint associated with training and running such a large language model is a concern for its environmental impact.Summary With the ever-growing advancement of AI technology, tools like ChatGPT can assist many professions, from beginners who want to learn a new language, to technical experts who work in the field of data science. Like any other tool, ChatGPT comes with its own shortcomings and challenges. Thus, since ChatGPT is still at its early stage of adoption, it is advisable for the AI community to work on its shortcomings and share its insights and solutions.   About the Author Matt Zand is the director of Coding Bootcamps and High School Technology Services (HSTS) which offers self-paced and live courses related to blockchain, software engineering, and AI.  HSTS is a member of the Linux Foundation and LF AI & Data. He is a leading author of Hands-on Smart Contract Development with Hyperledger Fabric book by O’Reilly Media. He has written many technical articles on blockchain development at sites such as IBM, Alibaba Cloud, Hyperledger, The Linux Foundation, and more. He is also the founder of three tech startups: RealBig, DC Web Makers, and GoReward. Currently, he is the Advisor at Zuma Dating Events. You can connect with him on LinkedIn: https://www.linkedin.com/in/matt-zand-64047871

Prompt Engineering and design play a very vital role in controlling the output of the model. Here are some best practices you can use to improve your prompts, as well as some practices you should avoid:Clarity: Use simple sentences and instructions that can easily be understood by ChatGPT. Conciseness: Favor short prompts and short sentences. This can be achieved by chunking your instructions into smaller sentences with clear intentions.Focus: Keep the focus of the prompt on a well-defined topic so that you don’t risk your output being too generic.Consistency: Maintain a consistent tone and language during the conversation so that you can ensure a coherent conversation.“Acting as…”: The hack of letting ChatGPT act as someone or something has proven to be extremely powerful. You can shorten the context you have to provide to the model by simply asking him to act like the person or system you want information from. We’ve already seen the interview-candidate example, where ChatGPT acted as an interviewer for a data scientist position. A very interesting prompt is that of asking ChatGPT to act as a console. Here is an example of it: Figure 1 – Example of ChatGPT acting as a Python console Note that the console, as it would be if it were real, is also reporting the error I made for the cycle, indicating that I was missing the brackets.There is a continuously growing list of Act as prompts you can try in the following GitHub repository: https://github.com/f/awesome-chatgpt-prompts.Considering the few-shot learning capabilities, there are some good tips for leveraging this feature in prompt designing. An ideal conversation is as follows: On the other hand, there are some things you should avoid while designing your prompt:Start with a concise, clear, and focused prompt. This will help you have an overview of the topic you want to discuss, as well as provide food for thought and potential expansion of particular elements. Here’s an exampleFigure 2 – Example of a clear and focused prompt to initiate a conversation with ChatGPTOnce you have identified the relevant elements in the discussion, you can ask ChatGPT to elaborate on them with much more focusFigure 3 – Example of a deep-dive follow-up question in a ChatGPT Sometimes, it might be useful to remember the model and the context in which you are inquiring, especially if the question might apply to various domainsFigure 4 – Example of a reminder about the context in a conversation with ChatGPTFinally, always in mind the limitations we mentioned in previous chapters. ChatGPT may provide partial or incorrect information, so it is always a good practice to double-check. One nice tip you could try is asking the model to provide documentation about its responses so that you can easily find proof of themFigure 5 – Example of ChatGPT providing documentation supporting its previous responses On the other hand, there are some things you should avoid while designing your prompt: Information overload: Avoid providing too much information to ChatGPT, since it could reduce the accuracy of the response.Open-ended questions: Avoid asking ChatGPT vague, open-ended questions. Prompts such as What can you tell me about the world? or Can you help me with my exam? are far too generic and will result in ChatGPT generating vague, useless, and sometimes hallucinated responses.Lack of constraints: If you are expecting an output with a specific structure, don’t forget to specify that to ChatGPT! If you think about the earlier example of ChatGPT acting as an interviewer, you can see how strict I was in specifying not to generate questions all at once. It took several tries before getting to the result since ChatGPT is thought to generate a continuous flow of text.Furthermore, as a general consideration, we still must remember that the knowledge base of ChatGPT is limited to 2021, so we should avoid asking questions about facts that occurred after that date. You can still provide context; however, all the responses will be biased toward the knowledge base before 2021. SummaryIn this article, we get to learn some strong principles that can help you learn how to prompt effectively.  We cover the importance of a good prompt, and all the important Do’s and Don'ts while designing a good prompt with a practical example. About the Author Valentina Alto graduated in 2021 in Data Science. Since 2020 she has been working in Microsoft as Azure Solution Specialist and, since 2022, she focused on Data&AI workloads within the Manufacturing and Pharmaceutical industry. She has been working on customers’ projects closely with system integrators to deploy cloud architecture with a focus on datalake house and DWH, data integration and engineering, IoT and real-time analytics, Azure Machine Learning, Azure cognitive services (including Azure OpenAI Service), and PowerBI for dashboarding. She holds a BSc in Finance and an MSc degree in Data Science from Bocconi University, Milan, Italy. Since her academic journey, she has been writing Tech articles about Statistics, Machine Learning, Deep Learning, and AI in various publications. She has also written a book about the fundamentals of Machine Learning with Python.LinkedInMedium

This article is the first part of a series of articles, please refer to Part 2  for learning how to add a Chatbot to the portfolio website you create in this article!Creating a personal portfolio is a great way for showcasing your skills and accomplishments as a developer or designer. Does that sound like a lot of work? Well… it doesn’t have to be. We can use ChatGPT to generate code snippets and obtain a lot of guidance throughout the process. This way you can build an impressive portfolio website with minimal effort.Here’s what you can do in around 10 prompts: Fig 1: Homepage Fig 2: Portfolio PageFig 3: Contact Page Not bad, right? And it even contains some features: The search functionality works and filters projects based on what you are typing.The testimonials and projects are not hard-coded, but dynamically populated with JavaScript (but not connected to a backend with a database, so for the purpose of this article, they are hard-coded there).Of course, this personal portfolio would need more content. And you could definitely use ChatGPT to generate a bit more content and descriptions for it. You should be adding in some professional pictures and images to complete your portfolio. However, this is a project that you can do in half a day to a day using ChatGPT. The prompt magic The prompts used to create the personal portfolio follow a few best practices that I’ll discuss later. In the overview below, asking to continue after cutting off a response is not included. If you’d like to see all the responses and the versions of the personal portfolio after each step, you can check out this GitHub repo: https://github.com/BrightBoost/brightboost-portfolio-chatgpt Here is the first prompt:I'm making a portfolio site, using HTML/CSS/JS. Can you help me write the initial framework for it?  I need a home page, in which I will introduce me, my passions, aspirations and study background. I also need a call-to-action directing visitors to explore my work.Then I need a portfolio page, in which I showcase my best projects. This will include case studies, and testimonials highlighting my contributions and accomplishments.Finally I need a contact page, which is a minimalist form and social media links in order to facilitate professional connections. And the second:That seems to be close to what I want. However, could you split these into 3 files? One for each page.The third, as you can see, a lot of the heavy lifting in terms of content is done in the prompt here:Can you populate the pages with the following information?Name: Olivia MartinezStudy: Olivia recently graduated from the University of California, Berkeley with a Bachelor's degree in Computer Science.Aspirations: Olivia aspires to become a full-stack software engineer at a tech startup that focuses on environmental sustainability. She hopes to contribute her skills to develop innovative solutions for pressing environmental issues.Hobbies: In her free time, Olivia enjoys hiking, painting, and exploring the latest advancements in machine learning.Example Open Source Projects:- GreenRoute: A web application that optimizes travel routes based on carbon emissions.- EcoClicker: A browser extension that encourages users to reduce their digital carbon footprint.Additional Personal Details: Olivia has been an active volunteer at her local recycling center, where she has helped develop a digital platform to streamline recycling processes. This is what it looked like after this prompt:Fig 4: Homepage after initial promptsFig 5: Portfolio page after promptFig 6: Contact Page after promptThe fourth prompt was quite a challenge and it required going back and forward a bit and testing it until it was good. It was tempting to just modify it, but ChatGPT was supposed to create it here and it did eventually:Can you help me modify the following snippet? ```html      <h2>Portfolio</h2>      <div class="project">        <h3>GreenRoute</h3>        <p>A web application that optimizes travel routes based            on carbon emissions.</p>        <a href="#" class="project-link">View Case Study</a>        <div class="testimonials">          <p>Testimonial 1</p>          <p>Testimonial 2</p>        </div>      </div>       <div class="project">        <h3>EcoClicker</h3>        <p>A browser extension that encourages users to reduce            their digital carbon footprint.</p>        <a href="#" class="project-link">View Case Study</a>        <div class="testimonials">          <p>Testimonial 1</p>          <p>Testimonial 2</p>        </div>      </div>    ``` I'm not satisfied with the look. Could you make the following changes: - Each project is displayed in a card.- The project link looks like a button, in the bottom right.- The title is underlined, and a bit larger.- The page shows 2 columns of cards. Fig 7: Home page after refined promptingAnd here’s the fifth: I need to make sure that footer is always at the bottom of the page, can you provide a CSS snippet to make that work?This also needed second attempt because it wasn’t working. Don’t just say that it doesn’t work, but be specific:It doesn't seem to work. The page only uses about 50% of the screen, so the footer is still in the middle. After this, it looks like: Fig 8: Homepage after footer promptsThis is where things really got cool, but this needed a few tweaks in terms of output. Here was the first prompt to add JavaScript: I'd like to make the portfolio a bit more extendable. Can you write some JavaScript code that generates the portfolio page using an array of objects? For now just put the content directly in code. I forgot a few classes, so let’s prompt again: This works, but you've excluded the classes used in the CSS. As a reminder, this is how a single item should look:** code of the prompt omitted And after this it was good: It seems the 2 column layout is gone. I think this:```html<section id="portfolio"><div class="container" id="portfolio-container"></div></section>```Should contain an element with the class `project-grid` somewhere, which should create a grid. Can you modify the snippet? The last part was on the search bar, which required this prompt:I'd like to add a search bar to the portfolio page. It must search for the text in the title and body. I only want to look for the exact text. After each character it should update the list, filtering out any project that does not match the search text. Then there should be a button to clear the search bar, and show all projects. Can you add this to the JavaScript file? And that’s it! Of course, there are many ways to do this, but this is one way of how you can use ChatGPT to create a personal portfolio. Let’s see some best practices for your ChatGPT prompts, to help you with using it to create your personal portfolio.Best practices for ChatGPT prompts There are some best practices I figured out when working with ChatGPT. Let’s go over them before seeing the prompts used for the personal portfolio.Be specific and clear: Make sure your prompt leaves little room for interpretation. For example, the prompt:Help me with a grid layout.Is not going to help you as much as:For this piece of HTML containing bootstrap cards provide a CSS snippet for a responsive 3-column grid layout with a 20px gap between columns: ** insert your HTML snippet here **Include relevant context and background information: Give the AI enough information to understand the problem or task and help you to its best ability. Don’t ask:How do I convert a date string to a Date object?But ask:  I have a JSON object with date and value properties. How do I convert the date property to a JavaScript Date object?Ask one question at a time: Keep your prompts focused and avoid asking multiple questions in one prompt.Make sure ChatGPT completes its answer before asking the next question: Sometimes it cuts off the result. You can ask it to continue and it will. That’s harder when you’re further down the line.Test the result after every step: Related to the previous tip, but make sure to test the result after every step. This way you can provide feedback on the outcome and it can easily adjust still. Step? Yes! Break down big projects into smaller tasks: Divide your project into manageable steps, and ask the AI to complete each task separately.Bonus tip: You can even ask ChatGPT for help on how to break your project into smaller tasks and make these tasks very detailed. Then go ahead and ask it to do one task at a time.The good news is these tips are actually great interaction tips with humans as well! I bet you’d like to see some of the prompts used to create the personal portfolio, so let’s dive in. Author BioMaaike van Putten is an experienced software developer and Pluralsight, LinkedIn Learning, Udemy, and Bright Boost instructor. She has a passion for software development and helping others get to the next level in their career.You can follow Maaike on:LinkedInTraining Courses

This article provides a quick guide to using the OpenAI API to jump-start ChatGPT. The guide includes instructions on how to use a microphone to speak to ChatGPT and how to create a ChatGPT request with variables. Additionally, the article explains how to use Google gTTS, a text-to-speech tool, to listen to ChatGPT's response. By following these steps, you can have a more interactive experience with ChatGPT and make use of its advanced natural language processing capabilities. We’re using the GPT-3.5-Turbo architecture in this example. We are also running the examples within Google Colab, but they should be applicable to other environments. In this article, we’ll cover: Installing OpenAI, your API key, and Google gTTS for Text-to-SpeechGenerating content with ChatGPTSpeech-to-text ChatGPT's responseTranscribing with WhisperTo understand GPT-3 Transformers in detail, read Transformers for NLP, 2nd Edition 1. Installing OpenAI, gTTS, and your API Key There are a few libraries that we’ll need to install into Colab for this project. We’ll install them as required, starting with OpenAI. Installing and Importing OpenAI To start using OpenAI's APIs and tools, we'll need to install the OpenAI Python package and import it into your project. To do this, you can use pip, a package manager for Python. First, make sure you have pip installed on your system. !pip install --upgrade pipNext, run the following script in your notebook to install the OpenAI package. It should come pre-installed in Colab:#Importing openaitry:import openaiexcept:!pip install openaiimport openai Installing gTTS Next, install Google gTTS a Python library that provides an easy-to-use interface for text-to-speech synthesis using the Google Text-to-Speech API:#Importing gTTStry:from gtts import gTTSexcept:!pip install gTTS   from gtts import gTTS API Key Finally, import your API key. Rather than enter your key directly into your notebook, I recommend keeping it in a local file and importing it from your script. You will need to provide the correct path and filename in the code below.from google.colab import drivedrive.mount('/content/drive')f = open("drive/MyDrive/files/api_key.txt", "r")API_KEY=f.readline()f.close()#The OpenAI Keyimport osos.environ['OPENAI_API_KEY'] =API_KEYopenai.api_key = os.getenv("OPENAI_API_KEY") 2. Generating Content Let’s look at how to pass prompts into the OpenAI API to generate responses. Speech to text When it comes to speech recognition, Windows provides built-in speech-to-text functionality. However, third-party speech-to-text modules are also available, offering features such as multiple language support, speaker identification, and audio transcription. For simple speech-to-text, this notebook uses the built-in functionality in Windows. Press Windows key + H to bring up the Windows speech interface. You can read the documentation for more information.Note: For this notebook, press Enter when you have finished asking for a request in Colab. You could also adapt the function in your application with a timed input function that automatically sends a request after a certain amount of time has elapsed. Preparing the Prompt Note: you can create variables for each part of the OpenAI messages object. This object contains all the information needed to generate a response from ChatGPT, including the text prompt, the model ID, and the API key. By creating variables for each part of the object, you can make it easier to generate requests and responses programmatically. For example, you could create a prompt variable that contains the text prompt for generating a response. You could also create variables for the model ID and API key, making it easier to switch between different OpenAI models or accounts as needed.For more on implementing each part of the messages object, take a look at: Prompt_Engineering_as_an_alternative_to_fine_tuning.ipynb.Here’s the code for accepting the prompt and passing the request to OpenAI:#Speech to text. Use OS speech-to-text app. For example,   Windows: press Windows Key + H def prepare_message():#enter the request with a microphone or type it if you wish  # example: "Where is Tahiti located?"  print("Enter a request and press ENTER:")  uinput = input("")  #preparing the prompt for OpenAI   role="user"  #prompt="Where is Tahiti located?" #maintenance or if you do not want to use a microphone  line = {"role": role, "content": uinput}  #creating the message   assert1={"role": "system", "content": "You are a helpful assistant."}  assert2={"role": "assistant", "content": "Geography is an important topic if you are going on a once in a lifetime trip."}  assert3=line  iprompt = []  iprompt.append(assert1)  iprompt.append(assert2)  iprompt.append(assert3)  return iprompt#run the cell to start/continue a dialogiprompt=prepare_message() #preparing the messages for ChatGPTresponse=openai.ChatCompletion.create(model="gpt-3.5-turbo",messages=iprompt) #ChatGPT dialogtext=response["choices"][0]["message"]["content"] #response in JSONprint("ChatGPT response:",text) Here's a sample of the output: Enter a request and press ENTER:Where is Tahiti locatedChatGPT response: Tahiti is located in the South Pacific Ocean, specifically in French Polynesia. It is part of a group of islands called the Society Islands and is located approximately 4,000 kilometers (2,500 miles) south of Hawaii and 7,850 kilometers (4,880 miles) east of Australia. 3. Speech-to-text the response GTTS and IPython Once you've generated a response from ChatGPT using the OpenAI package, the next step is to convert the text into speech using gTTS (Google Text-to-Speech) and play it back using  IPython audio.from gtts import gTTSfrom IPython.display import Audiotts = gTTS(text)tts.save('1.wav')sound_file = '1.wav'Audio(sound_file, autoplay=True) 4. Transcribing with Whisper If your project requires the transcription of audio files, you can use OpenAI’s Whisper.First, we’ll install the ffmpeg audio processing library. ffmpeg is a popular open-source software suite for handling multimedia data, including audio and video files:!pip install ffmpegNext, we’ll install Whisper:!pip install git+https://github.com/openai/whisper.git With that done, we can use a simple command to transcribe the WAV file and store it as a JSON file with the same name:!whisper  1.wavYou’ll see Whisper transcribe the file in chunks:[00:00.000 --> 00:06.360]  Tahiti is located in the South Pacific Ocean, specifically in the archipelago of society[00:06.360 --> 00:09.800]  islands and is part of French Polynesia.[00:09.800 --> 00:22.360]  It is approximately 4,000 miles, 6,400 km, south of Hawaii and 5,700 miles, 9,200 km,[00:22.360 --> 00:24.640]  west of Santiago, Chile.Once that’s done, we can read the JSON file and display the text object:import json with open('1.json') as f:     data = json.load(f) text = data['text'] print(text)This gives the following output:Tahiti is located in the South Pacific Ocean, specifically in the archipelago of society islands and is part of French Polynesia. It is approximately 4,000 miles, 6,400 km, south of Hawaii and 5,700 miles, 9,200 km, west of Santiago, Chile. By using Whisper in combination with ChatGPT and gTTS, you can create a fully featured AI-powered application that enables users to interact with your system using natural language inputs and receive audio responses. This might be useful for applications that involve transcribing meetings, conferences, or other audio files. About the Author Denis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive natural language processing (NLP) chatbots applied as an automated language teacher for Moet et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an advanced planning and scheduling (APS) solution used worldwide.You can follow Denis on LinkedIn:  https://www.linkedin.com/in/denis-rothman-0b034043/Copyright 2023 Denis Rothman, MIT License

This article is an excerpt from the book, Machine Learning with Microsoft Power BI, by Greg Beaumont. This book is designed for data scientists and BI professionals seeking to improve their existing solutions and workloads using AI.Data description generation plays a vital role in understanding complex datasets, but it can be a time-consuming task. Enter ChatGPT, an advanced AI language model developed by OpenAI. Trained on extensive text data, ChatGPT demonstrates impressive capabilities in understanding and generating human-like responses. In this article, we explore how ChatGPT can revolutionize data analysis by expediting the creation of accurate and coherent data descriptions. We delve into its training process, architecture, and potential applications in fields like research, journalism, and business analytics. While acknowledging limitations, we unveil the transformative potential of ChatGPT for data interpretation and knowledge dissemination. Our first step will be to identify a suitable use case for leveraging the power of GPT models to generate descriptions of elements of FAA Wildlife Strike data. Our objective is to unlock the potential of external data by creating prompts for GPT models that can provide detailed information and insights about the data we are working with. Through this use case, we will explore the value that GPT models can bring to the table when it comes to data analysis and interpretation.For example, a description of the FAA Wildlife Strike Database by ChatGPT might look like this: Figure 1 – OpenAI ChatGPT description of FAA Wildlife Strike Database Within your solution using the FAA Wildlife Strike database, you have data that could be tied to external data using the GPT models. A few examples include additional information about:AirportsFAA RegionsFlight OperatorsAircraftAircraft EnginesAnimal SpeciesTime of Year When the scoring process for a large number of separate rows in a dataset is automated, we can use a GPT model to generate descriptive text for each individual row. It is worth noting that ChatGPT's approach varies from this, as it operates as a chatbot that calls upon different GPT models and integrates past conversations into future answers. Despite the differences in how GPT models will be used in the solution, ChatGPT can still serve as a valuable tool for testing various use cases.When using GPT models, the natural language prompts that are used to ask questions and give instructions will impact the context of the generated text. Prompt engineering is a topic that has surged in popularity for OpenAI and LLMs. The following prompts will provide different answers when using “dogs” as a topic for a GPT query:Tell me about dogs:From the perspective of an evolutionary biologist, tell me about dogs:Tell me the history of dogs:At a third-grade level, tell me about dogs:When planning for your use of OpenAI on large volumes of data, you should test and evaluate your prompt engineering strategy. For this book, the use cases will be kept simple since the goal is to teach tool integration with Power BI. Prompt engineering expertise will probably be the topic of many books and blogs this year. You can test different requests for a description of an FAA Region in the data: Figure 2 – Testing the utility of describing an FAA Region using OpenAI ChatGPT You can also combine different data elements for a more detailed description. The following example combines data fields with a question to ask “Tell me about the Species in State in Month”: Figure 3 – Using ChatGPT to test a combination of data about Species, State, and Month There are many different options to consider. To combine a few fields of data and provide useful context about the data, you decide to plan a use case for describing the aircraft and operator. An example can be tested with the following formula in OpenAI ChatGPT such as “Tell me about the airplane model Aircraft operated by the Operator in three sentences." Here is an example using data from a single row of the FAA Wildlife Strike database: Figure 4 – Information about an airplane in the fleet of an operator as described by OpenAI ChatGPT From a prompt engineering perspective, asking this question for multiple reports in the FAA Wildlife Strike database would require running the following natural language query on each row of data (column names are depicted with brackets): Tell me about the airplane model [Aircraft] operated by [Operator] in three sentences:SummaryThis article explores how ChatGPT expedites the generation of accurate and coherent data descriptions. Unveiling its training process, architecture, and applications in research, journalism, and business analytics, we showcase how ChatGPT revolutionizes data interpretation and knowledge dissemination. Acknowledging limitations, we highlight the transformative power of this AI technology in enhancing data analysis and decision-making. Author BioGreg Beaumont is a Data Architect at Microsoft; Greg is an expert in solving complex problems and creating value for customers. With a focus on the healthcare industry, Greg works closely with customers to plan enterprise analytics strategies, evaluate new tools and products, conduct training sessions and hackathons, and architect solutions that improve the quality of care and reduce costs. With years of experience in data architecture and a passion for innovation, Greg has a unique ability to identify and solve complex challenges. He is a trusted advisor to his customers and is always seeking new ways to drive progress and help organizations thrive. For more than 15 years, Greg has worked with healthcare customers who strive to improve patient outcomes and find opportunities for efficiencies. He is a veteran of the Microsoft data speaker network and has worked with hundreds of customers on their data management and analytics strategies.You can follow Greg on his LinkedIn 

This article is an excerpt from the book, Machine Learning with Microsoft Power BI, by Greg Beaumont. This book is designed for data scientists and BI professionals seeking to improve their existing solutions and workloads using AI. In the ever-expanding landscape of data analysis, the ability to summarize vast amounts of information concisely and accurately is invaluable. Enter ChatGPT, an advanced AI language model developed by OpenAI. In this article, we delve into the realm of data summarization with ChatGPT, exploring how this powerful tool can revolutionize the process of distilling complex datasets into concise and informative summaries.Numerous databases feature free text fields that comprise entries from a diverse array of sources, including survey results, physician notes, feedback forms, and comments regarding incident reports for the FAA Wildlife Strike database that we have used in this book. These text entry fields represent a wide range of content, from structured data to unstructured data, making it challenging to extract meaning from them without the assistance of sophisticated natural language processing tools. The Remarks field of the FAA Wildlife Strike database contains text that was presumably entered by people involved in filling out the incident form about an aircraft striking wildlife. A few examples of the remarks for recent entries are shown in Power BI in the following screenshot: Figure 1 – Examples of Remarks from the FAA Wildlife Strike Database You will notice that the remarks have a great deal of variability in the format of the content, the length of the content, and the acronyms that were used. Testing one of the entries by simply adding a statement at the beginning to “Summarize the following:” yields the following result: Figure 2 – Summarizing the remarks for a single incident using ChatGPT Summarizing data for a less detailed Remarks field yields the following results: Figure 3 – Summarization of a sparsely populated results field In order to obtain uniform summaries from the FAA Wildlife Strike data's Remarks field, one must consider entries that vary in robustness, sparsity, completeness of sentences, and the presence of acronyms and quick notes. The workshop accompanying this technical book is your chance to experiment with various data fields and explore diverse outcomes. Both the book and the Packt GitHub site will utilize a standardized format as input to a GPT model that can incorporate event data and produce a consistent summary for each row. An example of the format is as follows:  Summarize the following in three sentences: A [Operator] [Aircraft] struck a [Species]. Remarks on the FAA report were: [Remarks]. Using data from an FAA Wildlife Strike Database event to test this approach in OpenAI ChatGPT is shown in the following screenshot: Figure 4 – OpenAI ChatGPT testing a summarization of the remarks field Next, you test another scenario that had more robust text in the Remarks field: Figure 5 – Another scenario with robust remarks tested using OpenAI ChatGPT SummaryThis article explores how ChatGPT can revolutionize the process of condensing complex datasets into concise and informative summaries. By leveraging its powerful language generation capabilities, ChatGPT enables researchers, analysts, and decision-makers to quickly extract key insights and make informed decisions. Dive into the world of data summarization with ChatGPT and unlock new possibilities for efficient data analysis and knowledge extraction. Author Bio:Greg Beaumont is a Data Architect at Microsoft; Greg is an expert in solving complex problems and creating value for customers. With a focus on the healthcare industry, Greg works closely with customers to plan enterprise analytics strategies, evaluate new tools and products, conduct training sessions and hackathons, and architect solutions that improve the quality of care and reduce costs. With years of experience in data architecture and a passion for innovation, Greg has a unique ability to identify and solve complex challenges. He is a trusted advisor to his customers and is always seeking new ways to drive progress and help organizations thrive. For more than 15 years, Greg has worked with healthcare customers who strive to improve patient outcomes and find opportunities for efficiencies. He is a veteran of the Microsoft data speaker network and has worked with hundreds of customers on their data management and analytics strategies.You can follow Greg on LinkedIn

Identifying inconsistencies and inaccuracies in the data is a vital part of the data analysis process. ChatGPT is a natural language processing tool powered by AI that enables users to have human-like conversations and helps them complete tasks quickly. In this article, we'll focus on how chatGPT can make the process of data cleansing and cleaning more efficient. Data Cleansing/Cleaning with ChatGPT Given the volume, velocity, and variety of data we deal with nowadays, manually carrying out the data cleansing task is a very time-consuming process. Data cleansing, the removal of duplicate data, data validity, uniqueness, consistency, and correctness are all steps taken to increase the quality of the data. Better business insights and the ability for business users to make wise decisions are provided by cleansed data. Data cleansing activities go via a series of steps, starting with gathering the data and ending with integrating, producing, and normalizing the data, as shown in the image below: Image 1: Data cleansing cycle  The majority of corporate organizations carry out the following tasks as part of the exploratory data analysis's data cleansing procedure: Identify and clean up Duplicate Values Fill Null Values with a default valueRectify and Correct inconsistent dataStandardising date formats Standardising  name or addressArea codes out of phone numbersFlattening nested data structuresErasing incomplete dataDetecting conflicts in the database The strength of ChatGPT allows us to perform time-consuming and extremely boring tasks like data purification with ease. Let's use the example of employee details for the banking industry to better comprehend it which has columns: Employee ID, Employee Name, Department Name, and Joining Date. While reviewing the data, we discovered a number of data quality concerns that must be resolved before we can truly use this data for analytics. Example: Employee Name is inconsistent - some instances use lowercase while others use uppercase letters. The data format is not uniform for the joining date column. Traditional Way of Working To clean up this data in Excel, we must manually construct the formulas and apply functions like TRIM, UPPER, or LOWER before using it for analytics. It calls for development work, and upkeep of Excel logic without version control, history, etc. Sounds extremely tedious, isn’t it? Working with ChatGPT We can utilize ChatGPT to automate the aforementioned data purification operation by implementing some Python code. In this example, we'll use the ChatGPT Python code to demonstrate how to standardize the name for the employee's name and the date format for the joining date.ChatGPT prompt:Here is the prompt that we can provide in the text format, in case you plan to copy and paste:             Employee ID | Employee Name | Department Name | Joining Date            214                   john Root                  HR                             1-06-2003            435                   STEVE Smith             Retail                          21-Feb-05            654                   Sachin WALA            OPSI                           25-July-1999 Above is the employee data source which should be cleaned. Employee names are not consistent, and the joining date is not in a uniform date format. Generate a Python code to create accurate data. Image 2: Input to the ChatGPTWe pass a dataset and a description of how and for which columns we want to clean the data as seen in the image above. Output from ChatGPTChatGPT automatically creates Python code with a variety of generic functions to clean the specified column in accordance with our specifications. The ChatGPT tool's output Python code is shown below.      Image 3: Output Python code from ChatGPT After running the Python code generated by ChatGPT on the stated data, ChatGPT also displays a sample result on the data here. It is clear that employee names are now uniform, and the joining date is likewise shown using a common date format.             Image 4 : Sample output from ChatGPT This Python code can be used to clean any data source in the future when we need to do so, not just the employee dataset. Therefore, using ChatGPT's capabilities, we can develop a fully automated data cleaning process that is precise, effective, and totally automated.There are also tools on the market like RATH, which has an integration with ChatGPT, to simplify the data analysis workflow and increase your productivity without putting in a lot of manual work if you are having trouble with a large volume of data and need to spend a lot of time performing the data cleaning activity ConclusionThis article gave you a fundamental grasp of the data cleaning/cleansing procedure, which will enable you to use the data to make more trustworthy decisions. The most effective method for using ChatGPT to clean your data simply and effectively for any data quantities. Author Bio:Sagar Lad is a Cloud Data Solution Architect with a leading organisation 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.You can follow Sagar on - Medium, Amazon, LinkedIn

This article is an excerpt from the book Modern Generative AI with ChatGPT and OpenAI Models, by Valentina Alto. This book will provide you with insights into the inner workings of the LLMs and guide you through creating your own language models. Information retrieval and competitive intelligence are fields where ChatGPT is a game-changer. It can retrieve information from its knowledge base and reframe it in an original way.One example is using ChatGPT as a search engine to provide summaries, reviews, and recommendations for books:  Alternatively, we could ask for some suggestions for a new book we wish to read based on our preferences:  If we design the prompt with specific information, ChatGPT can serve as a tool for pointing us towards the right references for research or studies. For example, asking ChatGPT to list relevant references for feedforward neural networks:  ChatGPT can also be useful for competitive intelligence. For example, generating a list of existing books with similar content:  Or providing advice on how to be competitive in the market:  ChatGPT can also suggest improvements regarding book content to make it stand out:  Overall, ChatGPT can be a valuable assistant for information retrieval and competitive intelligence. However, it's important to remember that the knowledge base cutoff is 2021, so real-time information may not be available. About the AuthorValentina Alto graduated in 2021 in Data Science. Since 2020 she has been working in Microsoft as Azure Solution Specialist and, since 2022, she focused on Data&AI workloads within the Manufacturing and Pharmaceutical industry. She has been working on customers’ projects closely with system integrators to deploy cloud architecture with a focus on datalake house and DWH, data integration and engineering, IoT and real-time analytics, Azure Machine Learning, Azure cognitive services (including Azure OpenAI Service), and PowerBI for dashboarding. She holds a BSc in Finance and an MSc degree in Data Science from Bocconi University, Milan, Italy. Since her academic journey she has been writing Tech articles about Statistics, Machine Learning, Deep Learning and AI on various publications. She has also written a book about the fundamentals of Machine Learning with Python.  You can connect with Valentina on:LinkedInMedium