Unlocking Data with Generative AI and RAG, Second Edition: Learn AI agent fundamentals with RAG-powered memory, graph-based RAG, and intelligent recall

Keith Bourne

As AI continues to permeate various industries and domains, understanding and mastering retrieval-augmented generation (RAG) has become increasingly crucial for developers, researchers, and businesses alike. RAG enables AI systems to go beyond the limitations of their training data and access up-to-date and domain-specific information, making them more versatile, adaptable, and valuable in real-world scenarios.

In this 2nd edition, readers will learn how to design RAG so it seamlessly interacts with agentic memory, semantic caches, knowledge graphs, and other critical components of AI workflows. Step by step, it will not only show you how to implement RAG but also explains the underlying concepts so you can adapt as the field evolves and unlock advanced capabilities for your AI applications.

The book is structured into three parts: Part 1, Introduction to RAG, introduces you to the fundamentals of RAG, covering its core concepts, advantages, challenges, and practical applications across various industries. Part 2, Components of RAG, takes a deeper dive into the essential building blocks of RAG systems and how to implement them using LangChain. Part 3, Implementing Agentic RAG, builds directly on the foundation established in previous parts, preparing readers for the cutting edge of agentic AI development.