An architecture or technique that enhances Large Language Models (LLMs) by giving them access to external, authoritative knowledge bases (like a company's internal documents or up-to-date web data) before generating a response. RAG grounds the LLM's output in facts, which reduces "hallucinations" and allows the model to access current, domain-specific information without expensive retraining.

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