Production RAG Engineering: Build Accurate, Scalable Retrieval Systems That Actually Work (en Inglés)
Reseña del libro "Production RAG Engineering: Build Accurate, Scalable Retrieval Systems That Actually Work (en Inglés)"
RAG looks simple in a demo: embed documents, store vectors, retrieve a few chunks, and send them to an LLM. Production systems are not that simple. Production RAG Engineering is a practical engineering guide to building retrieval-augmented generation systems that remain accurate, observable, secure, and scalable beyond the prototype stage. Rather than treating a vector database as a magic knowledge layer, this book approaches RAG as what it really is: a combination of information retrieval, data engineering, evaluation, security, and distributed systems design. You will learn how to design and improve the complete retrieval pipeline, including: • document ingestion, parsing, cleaning, and normalization • structure-aware and contextual chunking • embedding model selection and vector indexing • metadata design and filtered retrieval • BM25, semantic search, and hybrid retrieval • query rewriting, multi-query retrieval, and HyDE • reranking and cross-encoder architectures • context assembly and lost-in-the-middle mitigation • citations, provenance, and grounded generation • long-context models versus retrieval • tables, SQL, structured data, and multimodal RAG • agentic retrieval architectures • access-control-aware retrieval and tenant isolation • evaluation datasets and retrieval metrics • answer faithfulness and hallucination analysis • latency, caching, cost, observability, and drift • production debugging and scaling strategies Throughout the book, one system evolves from naive top-k vector search into a stronger production architecture. The examples use Python and vendor-independent engineering principles, with representative technologies where implementation details are useful. You will also see where common approaches fail, how to measure those failures, and how to decide whether a more sophisticated retrieval technique is actually justified. The book concludes with production checklists, troubleshooting decision trees, architecture review questions, implementation patterns, a glossary, and practical interview questions. For Applied AI engineers, ML engineers, backend engineers, platform engineers, and architects who want to build RAG systems that actually work in production.
