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

Get hands-on with GPT 3.5, GPT 4, LangChain, Llama 2, Falcon LLM and more, to build LLM-powered sophisticated AI applications

Key FeaturesEmbed LLMs into real-world applicationsUse LangChain to orchestrate LLMs and their components within applicationsGrasp basic and advanced techniques of prompt engineeringBook 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.

What you will learnExplore the core components of LLM architecture, including encoder-decoder blocks and embeddingsUnderstand the unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLMUse AI orchestrators like LangChain, with Streamlit for the frontendGet familiar with LLM components such as memory, prompts, and toolsLearn how to use non-parametric knowledge and vector databasesUnderstand the implications of LFMs for AI research and industry applicationsCustomize your LLMs with fine tuningLearn about the ethical implications of LLM-powered applicationsWho this book is 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.

Table of ContentsIntroduction to Large Language ModelsLLMs for AI-Powered ApplicationsChoosing an LLM for Your ApplicationPrompt EngineeringEmbedding LLMs within Your ApplicationsBuilding Conversational ApplicationsSearch and Recommendation Engines with LLMsUsing LLMs with Structured DataWorking with CodeBuilding Multimodal Applications with LLMsFine-Tuning Large Language ModelsResponsible AIEmerging Trends and Innovations

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Publisher ‏ : ‎ Packt Publishing
Publication date ‏ : ‎ May 22, 2024
Language ‏ : ‎ English
Print length ‏ : ‎ 342 pages
ISBN-10 ‏ : ‎ 1835462316
ISBN-13 ‏ : ‎ 978-1835462317
Item Weight ‏ : ‎ 1.3 pounds
Dimensions ‏ : ‎ 7.5 x 0.78 x 9.25 inches
Best Sellers Rank: #66,658 in Books (See Top 100 in Books) #5 in Word Processing Books #30 in Computer Neural Networks #41 in Natural Language Processing (Books)
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