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- RAG (Retrieval-Augmented Generation) combines an information retrieval step (typically vector search over an embedding store) with an LLM generation step. The retrieved context is inserted into the prompt so the model can ground answers in up-to-date or proprietary data without re-training. RAG is the dominant pattern for enterprise LLM applications.