Learning path · Production RAG · 50
Retrieval-Augmented Generation
Retrieve relevant external documents at query time, inject them into the prompt, then generate an answer grounded in that evidence.
Why it matters
- Primary pattern for organizational knowledge without retraining.
- Separates reasoning (LLM) from facts (retrieval index).
- Requires eval on faithfulness, not fluency alone.
Key ideas
- Retrieve then generate
- Grounding
- Citations
RAG connects non-parametric memory to the reasoning engine: embed the question, fetch chunks, assemble context, generate with cite-or-refuse instructions. Naive RAG stops there; production RAG adds hybrid search, reranking, query transforms, observability, and guardrails. Success is measured by faithful answers backed by retrieved passages—not eloquent guesses when retrieval fails. Ship refusal paths when retrieval confidence is low; users prefer honest limits over fluent wrong answers backed by irrelevant citations. Ship only after eval gates pass on representative production failures.
Updated 2026-08-09 · Full learning path