RAG gives a large language model access to your own documents at answer time. It retrieves the most relevant passages from your knowledge base and feeds them to the model, so answers are grounded in your data — with sources — instead of the model's training alone. It's the main technique for reducing hallucinations. See LLM Optimisation →
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