[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_29":-5,"_39":-5,"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5,"_12":-5},"book",{"_13":14,"_15":16,"_17":18,"_19":20,"_21":22,"_23":24},"currentUser","formToken","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvODQ4MzY","title","The Art and Science of RAG","state","published","contactAuthorMethod","email","aboutText","

Large language models are powerful, but they hallucinate, their knowledge is frozen in time, and they cannot natively access private or proprietary information. Retrieval-Augmented Generation (RAG) addresses all of these problems by combining external knowledge retrieval with language model generation. This book takes you from the foundational concepts behind RAG through every design decision that matters in production: embeddings, chunking, indexing, hybrid search, reranking, context construction, generation control, evaluation, security, and system architecture. You will understand not only what each component does, but why it exists, how it works internally, the trade-offs between competing approaches, and when each technique is appropriate. Through complete runnable code examples and practical guidance, this book functions both as a first-principles textbook and a reference you will use when building real systems.

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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