Deploying a Production-Ready RAG System: An Implementation Guide

An implementation guide covering the full RAG pipeline — ingestion, chunking, embedding, retrieval, and generation — with the governance and evaluation steps most tutorials skip.

Ingestion and chunking

Use structure-aware chunking (respecting headings, tables, and document sections) rather than fixed-length character chunking, which materially improves retrieval quality on structured enterprise documents.

Retrieval and re-ranking

Combine vector similarity search with a keyword-based method and a re-ranking step for production quality; pure vector search alone consistently underperforms hybrid retrieval on enterprise document sets with domain-specific terminology.

Evaluation before launch

Build a golden test set of realistic queries with known-correct answers before launch, and re-run it after every change to chunking strategy, embedding model, or prompt template — RAG quality regressions are easy to introduce invisibly.

Access control

Filter retrieval results by the requesting user’s document-level permissions before they ever reach the language model — this must happen at the retrieval layer, not as a post-hoc content filter on the generated answer.