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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

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Javier Soques Jul 04, 2024

Good learning resource for understanding LLMs

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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.

Amazon Verified review

Samuel de Zoete Jul 17, 2024

The book is well written and well structured. The introduction into LLMs is exceptionally good and timeless! I really think the author nailed the theory and description of LLMs. I have read many other versions of this topic (books, medium), which were overcomplicated as if that is a requirement to write something.As with any book on LLM's there is a chance that some of the content (libraries, models, code and prompt templates) might be outdated as this literally change on a weekly if not daily basis and everybody is trying to be the flavor of the moment. Still, this doesn't change the general approach and principles to coding the LLM powered applications, using RAG (Retrieval Augmented Generation), Embedding techniques, retrieval techniques, etc.It's a gem, it's easy to follow and a must have for any serious LLM application developer.

Al Sep 04, 2024

This is a great book for any one wanting to learn about llm applications, it starts with basics and talks all the basic knowledge that one needs before delving into advanced topics, written in clear manner with examples a must read for gen ai materials.

kk Jul 21, 2024

I recently had the pleasure of reviewing the book "Building LLM Powered Applications" by Valentina Alto. If you want to build GenAI applications that work, this book is highly recommended. It offers many pro tips and design patterns that are very useful for building real-world applications. Here is what I found:Practical Roadmap: For those seeking guidance on building GenAI applications, this book offers a concise and practical roadmap based on real-world problems.Comprehensive Coverage: Despite the rapid evolution of the GenAI field, the book covers essential topics thoroughly, providing a solid foundation to keep pace with industry developments.Dual Perspective: The author caters to both GenAI beginners and experienced AI/ML practitioners, offering valuable insights for readers at different skill levels.Motivational Approach: Each topic is introduced with clear motivations, followed by relatable examples that help readers build intuition and understanding.Hands-on Learning: The book emphasizes practicality, with clear, followable instructions that allow readers to code and test concepts immediately.Comparative Analysis: By presenting both pre-GenAI and GenAI approaches, the author effectively illustrates the pros and cons of each method, helping readers connect the dots between traditional and modern techniques.In conclusion, "Building LLM Powered Applications" is an excellent resource for anyone looking to dive into the world of GenAI or enhance their existing AI/ML skills with practical LLM knowledge.

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

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