RAG from First Principles

Preface

Retrieval-augmented generation (RAG) has rapidly become one of the most important approaches for building reliable and intelligent AI systems. By combining large language models with external knowledge retrieval, RAG enables applications to generate responses that are more accurate, contextual, and grounded in enterprise data. In this book, you will explore the complete RAG pipeline from first principles, beginning with the foundations of embeddings, vector storage, and vector databases, before moving into advanced retrieval optimization and response generation strategies. The book explains not only how RAG systems work conceptually, but also how they are implemented in practical, production-ready environments.

You will first learn how ...