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RAG (retrieval-augmented generation)

RAG retrieves relevant text from your sources and passes it to a language model before generation. The point is to ground answers in documents you can name.

Architecture we actually ship

1. Ingest sources you have a right to use.
2. Chunk and tag so retrieval matches operator questions.
3. Embed and store; retrieve top passages at query time.
4. Generate only with those passages in context.
5. Show sources. Measure retrieval hits and grounded answers separately.

Reranking and hybrid search are added when simple retrieval fails the eval set—not as decoration.

Hire the build: RAG development. Definition: What is RAG?

Questions