RAG retrieves the most relevant passages or clips from a knowledge store, then hands them to the model as context for its answer. It curbs hallucination, keeps responses current, and lets a model use private data it was never trained on.
Definition
Giving a language model relevant facts fetched from your own data at query time, so its answers are grounded in real sources rather than memory alone.
RAG retrieves the most relevant passages or clips from a knowledge store, then hands them to the model as context for its answer. It curbs hallucination, keeps responses current, and lets a model use private data it was never trained on.
Also known as
retrieval-augmented generation
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