Retrieval-Augmented Generation (RAG) in Production: Build, Evaluate, Optimize, and Deploy Enterprise RAG Systems with Python

Build production-grade Retrieval-Augmented Generation systems with Python - from raw documents to reliable, evaluated, and deployable RAG applications. RAG is more than embedding documents and asking an LLM a question. A production system must retrieve the right evidence, enforce access controls, resist leakage, produce grounded answers, measure quality, handle changing corpora, and remain observable and recoverable in production. Retrieval-Augmented Generation in Production takes you through that complete engineering journey with a practical Python implementation and a deterministic companion project. You will learn how to: - Design index-time and query-time RAG pipelines with clear component contracts- Load and extract content from PDFs, DOCX, HTML, and Markdown- Normalize documents, preserve structure, and attach useful metadata- Design chunking strategies and understand their impact on retrieval quality- Build embeddings and vector indexes with FAISS and Qdrant- Combine dense and lexical retrieval using BM25 and reciprocal rank fusion- Apply metadata filtering and retrieval-time access controls- Improve precision with cross-encoder reranking- Rewrite and expand queries when the original question is difficult to retrieve- Assemble token-budgeted context while controlling duplication and ordering- Generate grounded answers with citations and explicit refusal behavior- Build golden sets and evaluate retrieval, groundedness, leakage, latency, and cost- Design deterministic evaluation harnesses that make results reproducible- Trace and operate RAG systems with production-oriented observability- Handle corpus updates, index generations, aliases, snapshots, rollback, and recovery- Build and deploy a production RAG API with FastAPI >A recurring Harbourline corpus and realistic production scenarios connect the chapters, while practical projects turn the architecture into working systems. The companion repository also provides the code, exercises, and reproducible implementation used throughout the book. Companion GitHub repository: masud-dot/production-rag-with-python Whether you are an AI engineer, Python developer, ML engineer, backend engineer, or QA/evaluation professional working with LLM applications, this book gives you a practical framework for moving RAG from a prototype into a system you can actually evaluate, operate, and improve. Build the pipeline. Measure the evidence. Control the failure modes. Ship RAG that can be trusted.