Retrieval-Augmented Generation (RAG) enhances the capabilities of large language models by enabling them to fetch real-time, relevant data from external sources, thereby mitigating hallucination and outdated knowledge.
While traditional LLMs are innovative, they suffer from hallucination, outdated knowledge, untraceable reasoning, and lack of domain-specific expertise, rendering them less reliable for specialized inquiries.
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