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AI hallucinations: Why LLMs make things up (and how to fix it)
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Advanced RAG builds upon this by introducing additional pre- and post-processing steps, including query expansion, subquery generation, Chain-of-Verification, and document reranking, to further refine the relevance of its retrieved chunks. Techniques such as selective context filtering, retrieval-augmented generation, chain-of-thought prompting, and task-specific modeling, significantly reduce hallucination risks, enhancing the reliability and trustworthiness of LLM outputs. As the field continues to evolve, these strategies will likely play a central role in developing AI systems that are both accurate and contextually aware, advancing the practical application of LLMs in diverse domains.
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