A Hands-On Guide to Building, Evaluating, and Scaling Retrieval-Augmented Systems Every large language model has the same blind spot: it answers from memory, not from truth. Ask it about last week's policy change, your company's internal pricing, or a fact buried in a document it never saw, and it won't say "I don't know." It will guess, fluently and confidently, and you'll have no way to tell the difference between insight and invention until it's already cost you something. Retrieval-Augmented Generation is the engineering discipline that closes that gap, and this book is the guide that takes you from understanding why it matters to actually shipping it. Jonathan Owens walks readers through the full arc of modern RAG: how language models really "know" things and why that breaks down, how to architect a retrieval pipeline from ingestion to generation, how chunking and embedding choices quietly make or break retrieval quality, and how hybrid search, reranking, and query rewriting turn a fragile prototype into something dependable. A complete hands-on build, written in plain Python with open-source tools, takes you from an empty folder to a working, cited, grounded question-answering system you fully understand line by line. From there, the book pushes into the frontier shaping production AI right now: agentic retrieval loops that plan and self-correct, GraphRAG and LazyGraphRAG for reasoning across entire document collections, multimodal retrieval over images and tables, and the evaluation, security, and operational discipline, faithfulness scoring, access control, prompt-injection defense, golden datasets, that separates a demo from a system you'd trust in production. Every chapter pairs clear conceptual grounding with real code, case studies, and exercises, so the ideas don't stay theoretical. Written for developers, data scientists, and technical leads who want more than buzzwords, this is a book about judgment as much as technique: knowing not just how to build RAG, but when, why, and how to know if it's actually working. Whether you're shipping your first retrieval-augmented feature or hardening one already in production, this is the reference you'll keep coming back to.
