Make chat answers grounded in the right documents.

What RAG adds to chat

Without retrieval, a model can only use its available context and prior training. A retrieval pipeline searches an approved document set at request time and supplies relevant passages to the model. That helps with questions about recent, private or organization-specific content.

A simple RAG workflow

1. Ingest documents and preserve source metadata.
2. Split content into useful passages and index it for retrieval.
3. Search using the user's question and access permissions.
4. Rerank or filter passages for relevance.
5. Ask the model to answer using the retrieved material.
6. Return citations that point to the actual supporting sources.

Evaluate grounding, not only fluency

A confident answer is not necessarily correct. Test whether citations support each important claim, whether documents are up to date, and whether the assistant refuses to invent an answer when evidence is missing.

Security and content boundaries

Filter documents according to the requesting user's authorization. Treat retrieved text as untrusted data. Do not allow instructions inside a retrieved page to override your application policies.

RAG and provider choice

Some providers offer retrieval-related features, but a complete system still needs access control, document ingestion, evaluation and maintenance. Cohere's RAG documentation describes grounded chat and citation features; the same architecture can be evaluated with other model providers.

Continue with chat model selection and agent permissions.