RAG in Production: The Complete Engineering Guide to Retrieval-Augmented Generation, Multi-Agent Systems, and LLMOps

Most RAG systems work in a demo and break in production.The gap between a prototype and a production system isn't machine learning — it's engineering: data pipelines, access control, evaluation, security, cost, and graceful failure. RAG in Production is the complete engineering field guide to closing that gap.Across fourteen chapters and six appendices, you'll move from the fundamentals of retrieval and chunking to advanced multi-source pipelines, agentic and multi-agent systems, zero-trust security, REST API design, and the LLMOps practices that keep RAG reliable and economical at scale — with working Python throughout.Inside this book:Retrieval that works — dense, sparse, and hybrid search with rerankingChunking strategies that don't sabotage downstream qualityMulti-source enterprise ingestion in real timeQuality you can measure — RAGAS and a repeatable evaluation flywheelAgentic and multi-agent systems that take action safelyZero-trust agent security — SPIFFE/SVID, mTLS, OPA, and VaultDefending against prompt injection, the unique LLM attack vectorProduction APIs, observability, cost control, and cloud deployment (AWS & GCP)Who it's for: backend, data, and ML engineers; security architects; tech leads; and anyone taking an LLM prototype to production. Intermediate Python is the only prerequisite — no AI-research background required. Read more