True or False: RAG can help reduce the risk of hallucination in an LLM.

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Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of language models by integrating external information retrieval mechanisms. This approach allows the model to fetch relevant data from a database or knowledge source in real-time, thereby providing contextually accurate information to base its responses on.

When a language model operates solely on its training data, it might generate plausible-sounding but factually incorrect statements, a phenomenon known as hallucination. By utilizing RAG, the model can access updated and verified data, which significantly minimizes the chances of generating false or misleading information. The retrieval step serves as a safeguard, ensuring that the output is informed by real-world data, leading to more accurate and trustworthy results.

This synergy of retrieval and generation, therefore, plays a crucial role in reducing hallucination risks in language models.

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