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Retrieval-augmented generation (RAG) is an AI technique that enhances the quality of LLM-generated responses by incorporating trusted external sources of knowledge. This improves accuracy by ensuring the model has access to the most current and reliable facts, reduces hallucination rates, and provides source attribution, increasing user trust in the output. RAG is particularly useful in LLM-based question-answering systems.