A technique that supplies a Large Language Model with passages retrieved from a trusted source (clinical guidelines, a knowledge base, a patient record) at the time of a query, so that its output is grounded in that material rather than in training data alone. Retrieval-augmented generation reduces, but does not eliminate, fabricated content, and it underpins many clinical question-answering and documentation tools.
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