Definition
RAG is an architecture that grounds an AI model's answers in your own retrieved documents, improving accuracy over the model's built-in knowledge.
RAG reduces hallucination but adds data-governance questions: what gets indexed, who can retrieve it, and whether access controls carry through to the answers. Treat the retrieval layer as part of your security and privacy perimeter.
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Benchside turns retrieval-augmented generation into the exact questions, exclusions, and lock-in math for your specific vendor - your first project is free.