Retrieval-Augmented Generation (RAG) — Canonical Workflow

What this reference supports

Retrieval-Augmented Generation (RAG) grounds a model's answers in your own documents or knowledge base by retrieving relevant context from a vector database before generation — the production-default pattern for trustworthy Q&A, internal search, and customer-facing assistants where unsupported answers are unacceptable.

Use this page to check when RAG fits your use case, which models, vector databases, and frameworks are compatible, and which architecture and benchmark references to review before committing to a retrieval stack.

Workflow fit here is provisional decision support based on goal, data, constraints, risk, and available evidence — it does not guarantee retrieval quality or answer accuracy. Evaluate the candidate stack on representative queries, including out-of-scope questions and stale or missing documents, before production use.