Building LLM Apps: 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 Features

• Embed LLMs into real-world applications
• Use LangChain to orchestrate LLMs and their components within applications
• Grasp basic and advanced techniques of prompt engineering

Book Description

Building 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 learn

• Core components of LLMs’ architecture, including encoder-decoders blocks, embedding and so on
• Get well-versed with unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLM
• Use AI orchestrators like LangChain, and Streamlit as frontend
• Get familiar with LLMs components such as memory, prompts and tools
• Learn non-parametric knowledge, embeddings and vector databases
• Understand the implications of LFMs for AI research, and industry applications
• Customize your LLMs with fine tuning
• Learn the ethical implications of LLM-powered applications

Who 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 Contents

1. Introduction to LLMs
2. LLMs for AI-powered applications
3. Choosing an LLM for your app
4. Embedding LLMs within apps
5. Building Conversational apps
6. Developing search and recommendation engines
7. Generative Text apps
8. LLMs on structured data
9. Generating Code and Structured Outputs
10. Building multi-modal Agents
11. Fine-tuning LLMs
12. Responsible AI
13. Emerging Trends and Innovations