📚🤖💬 RAG and Agents
💬 Gemini Prompt 1
📚 I’m interested in chapter 6 (RAG and Agents) of the book AI Engineering: Building Applications with Foundation Models. 💡 Can you recommend books that go broader and deeper on the design and implementation of 🔍 RAG and 🤖 agentic AI systems?
🏗️ Building on your interest in Chapter 6 of AI Engineering: Building Applications with Foundation Models, 📚 here are some books that can help you go broader and deeper into the design and implementation of RAG and agentic AI systems. 🚀 The field is moving quickly, 📰 so many of the most in-depth and practical resources are recent or upcoming publications.
📚 Focused on Retrieval-Augmented Generation (RAG)
- ❓📚 A Simple Guide to Retrieval Augmented Generation by Abhinav Kimothi: 💡 This book provides a comprehensive, yet easy-to-follow introduction to RAG. 🧩 It covers the essential components, including the indexing and generation pipelines, and moves into advanced strategies and evaluation methods. 💻 It’s designed for developers and data scientists who want a practical, code-focused approach to building their first RAG system.
- 📖 Mastering Retrieval-Augmented Generation: Advanced Techniques and Production-Ready Solutions for Enterprise AI by Ranajoy Bose: 🏢 As the title suggests, this book is geared towards building enterprise-grade RAG systems. 🚀 It goes beyond the basics to cover advanced topics like optimizing chunking strategies, 🧠 using different types of embedding models, and implementing more complex patterns like Graph RAG and Agentic RAG. ⚙️ It also addresses production-level concerns like evaluation, deployment, and security.
🤖 Focused on Agentic AI Systems
- 📖 🤖🧠⚙️💡 Building Agentic AI Systems: Create intelligent, autonomous AI agents that can reason, plan, and adapt by Anjanava Biswas and Wrick Talukdar: 💡 This book provides a detailed look at the core principles of agentic systems. 🧠 It covers how to design agents that can reason, plan, and use external tools. 🤝 It also explores advanced concepts like reflection, introspection, and multi-agent collaboration using the “coordinator, worker, and delegator” approach.
- 📖 AI Agents in Action by Micheal Lanham: 💻 This book focuses on building practical, production-ready AI agents. 🧩 It covers a framework for developing agents that handle real-world tasks, including implementing knowledge management and memory systems. ⚙️ The book provides hands-on examples using modern tools and libraries like LangChain, AutoGen, and CrewAI.
- 📖 Building Applications with AI Agents: Designing and Implementing Multi-Agent Systems by Michael Albada: 📚 This resource provides a practical and research-based guide to building both single- and multi-agent systems. 💡 It explores the core components and design principles of agents, with a focus on implementing collaborative, multi-agent systems.
📚🤖 RAG and Agents in Depth
- 🤖🧠🔗 Building AI Agents with LLMs, RAG, and Knowledge Graphs: A practical guide to autonomous and modern AI agents by Salvatore Raieli and Gabriele Iuculano: 💡 This book is a great option as it covers RAG as a core component of building agents. ⚙️ It provides a practical guide to engineering intelligent behavior by combining RAG with knowledge graphs and other agent-based architectures. 🚀 It dedicates chapters to advanced RAG techniques before moving on to how these systems can power more sophisticated agents.
- 📖 The LLM Engineering Handbook by Paul Iusztin and Maxime Labonne: 🛠️ While not exclusively about RAG and agents, this handbook is praised for its comprehensive coverage of the entire LLM stack. 💻 It is a very practical and hands-on guide that goes deep into the key skills and libraries needed for building and deploying LLM applications, including RAG and agents.