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        "question": "Can you explain how Retrieval-Augmented Generation (RAG) works from the sources provided?",
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            "answer": "Retrieval-Augmented Generation (RAG) is a technique in natural language processing that enhances the capabilities of generative language models by integrating real-time data retrieval. This approach addresses the limitations of traditional generative models, which rely solely on their training data and may produce outdated or incorrect information if the data is not current. RAG ensures that the responses generated are both contextually appropriate and grounded in factual information by incorporating real-time data from external sources.",
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