Every RAG demo looks the same: a folder of documents, a vector database, a few impressive answers in a meeting room. Then it ships — and the cracks appear. Answers that sound confident but cite the wrong source. Questions the documents clearly answer, met with "I don't know." A system that quietly got worse last month and nobody noticed until support tickets piled up. This book is for the engineers who've hit that wall and need to know exactly what to fix, and why. Jonathan Owens walks through the five places naive RAG breaks — representation, retrieval, query understanding, reasoning, and verification — and the specific, implementable techniques that fix each one: structure-aware chunking and contextual retrieval, hybrid search and reranking, query rewriting and decomposition, agentic loops that check their own evidence before answering, and knowledge graphs for the questions no single passage can answer alone. Real code, real case studies, and a rigorous evaluation framework tie every technique back to a measurable result, so you're never adding complexity on a hunch. The final chapters take the system the rest of the book built and make it survive production: caching, vector database scaling, latency budgeting, cost modeling, and CI/CD built for retrieval pipelines specifically. Whether you're rescuing a struggling pilot or architecting a system from scratch, Enhanced RAG gives you the diagnostic discipline to know which upgrade your system actually needs — and the engineering depth to build it right.
