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Retrieval-augmented generation, or RAG, retrieves external information relevant to a request and supplies that information to a generative model.

Retrieval can improve freshness, provenance, and task-specific grounding without changing the model’s learned parameters.

RAG does not guarantee correctness. Retrieval quality, context construction, and answer evaluation remain separate engineering concerns.