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Key benefits

Embed LLMs into real-world applications

Use LangChain to orchestrate LLMs and their components within applications

Grasp basic and advanced techniques of prompt engineering

Description

Building LLM Powered Applications delves into the fundamental concepts, cutting-edge technologies, and practical applications that LLMs offer, ultimately paving the way for the emergence of large foundation models (LFMs) that extend the boundaries of AI capabilities. The book begins with an in-depth introduction to LLMs. We then explore various mainstream architectural frameworks, including both proprietary models (GPT 3.5/4) and open-source models (Falcon LLM), and analyze their unique strengths and differences. Moving ahead, with a focus on the Python-based, lightweight framework called LangChain, we guide you through the process of creating intelligent agents capable of retrieving information from unstructured data and engaging with structured data using LLMs and powerful toolkits. Furthermore, the book ventures into the realm of LFMs, which transcend language modeling to encompass various AI tasks and modalities, such as vision and audio. Whether you are a seasoned AI expert or a newcomer to the field, this book is your roadmap to unlock the full potential of LLMs and forge a new era of intelligent machines.

Who is this book for?

Software engineers and data scientists who want hands-on guidance for applying LLMs to build applications. The book will also appeal to technical leaders, students, and researchers interested in applied LLM topics. We don’t assume previous experience with LLM specifically. But readers should have core ML/software engineering fundamentals to understand and apply the content.

What you will learn

Explore the core components of LLM architecture, including encoder-decoder blocks and embeddings

Understand the unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLM

Use AI orchestrators like LangChain, with Streamlit for the frontend

Get familiar with LLM components such as memory, prompts, and tools

Learn how to use non-parametric knowledge and vector databases

Understand the implications of LFMs for AI research and industry applications

Customize your LLMs with fine tuning

Learn about the ethical implications of LLM-powered applications

What do you get with a Packt Subscription?

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50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

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Frequently bought together

€37.99

€39.99

Transformers for Natural Language Processing and Computer Vision

€41.99

Total € 119.97

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Javier Dec 08, 2023

Great content!!

Subscriber review

Dag Aug 24, 2024

Excellent introduction to engineering LLM-based applications. Bummer that the code examples won't work with current langchain versions. Then again, fiddeling around with that framework is just another learning opportunity.

Javier Soques Jul 04, 2024

Good learning resource for understanding LLMs

Feefo Verified review

dr t Jun 01, 2024

As an advocate of LLMs, this looked like a book that I should read.The book is well-written, well-structured, and easy to read. In 13 chapters, it takes readers through fundamental concepts (e.g. transformers, backprop, embeddings), to practical applications (e.g. building a chatbot, search engines) and considerations (e.g. choosing an LLM, prompt engineering), ethical and responsible considerations, and also looks at the latest advancements in the field of gen AI.This reader found the chapter on Search and Recommendation Engines with LLMs most compelling. The chapter explores how large language models can improve recommendation systems through the use of embeddings and generative models, the reader also learns how to build their own recommendation system application, utilising state-of-the-art LLMs with LangChain as the framework. Fascinating stuff!In summary, if you work with LLMs this is a useful book that contains lot of useful knowledge and practical examples. Recommended.

Amazon Verified review

Saksham May 25, 2024

One of the standout features of this book is its emphasis on real-world applications. The author shares numerous case studies and practical examples that highlight how LLMs can be used to create intelligent applications across various domains, from customer service chatbots to advanced content generation tools. These examples are not just technically informative but also thoughtfully chosen to demonstrate the potential for LLMs to enhance human productivity and creativity.

Building LLM Powered Applications: Create intelligent apps and agents with large language models

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