Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant information from external knowledge bases and incorporating it into the generation process. This allows LLMs to produce more accurate, up-to-date, and contextually grounded responses, reducing hallucinations.
Retrieval-Augmented Generation (RAG) improves AI models, especially large language models, by letting them look up information from external sources before answering. This makes their responses more accurate, current, and reliable, preventing them from making things up.
RAG-LLM, Retrieval-Augmented LMs, RAG models
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