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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 DescriptionBuilding LLM Apps 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 readers 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 learnCore components of LLMs’ architecture, including encoder-decoders blocks, embedding and so onGet well-versed with unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLMUse AI orchestrators like LangChain, and Streamlit as frontendGet familiar with LLMs components such as memory, prompts and toolsLearn non-parametric knowledge, embeddings and vector databasesUnderstand the implications of LFMs for AI research, and industry applicationsCustomize your LLMs with fine tuningLearn 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 LLMsLLMs for AI-powered applicationsChoosing an LLM for your appEmbedding LLMs within appsBuilding Conversational appsDeveloping search and recommendation enginesGenerative Text appsLLMs on structured dataGenerating Code and Structured OutputsBuilding multi-modal AgentsFine-tuning LLMsResponsible AIEmerging Trends and Innovations
Key FeaturesEmbed LLMs into real-world applicationsUse LangChain to orchestrate LLMs and their components within applicationsGrasp basic and advanced techniques of prompt engineeringBook DescriptionBuilding LLM Apps 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 readers 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 learnCore components of LLMs’ architecture, including encoder-decoders blocks, embedding and so onGet well-versed with unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLMUse AI orchestrators like LangChain, and Streamlit as frontendGet familiar with LLMs components such as memory, prompts and toolsLearn non-parametric knowledge, embeddings and vector databasesUnderstand the implications of LFMs for AI research, and industry applicationsCustomize your LLMs with fine tuningLearn 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 LLMsLLMs for AI-powered applicationsChoosing an LLM for your appEmbedding LLMs within appsBuilding Conversational appsDeveloping search and recommendation enginesGenerative Text appsLLMs on structured dataGenerating Code and Structured OutputsBuilding multi-modal AgentsFine-tuning LLMsResponsible AIEmerging Trends and Innovations
580 pages, Kindle Edition
Published May 22, 2024
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Displaying 1 of 1 review
March 7, 2025
The book strikes a good balance between theory and hands-on implementation. Author provides clear explanations of LLM concepts while demonstrating their application through well-structured example apps. The book covers frameworks like LangChain and practical prompt engineering techniques, making it a useful resource for developers looking to integrate LLMs into real-world solutions. A solid read for beginners and intermediate practitioners exploring AI-powered applications.
Displaying 1 of 1 review