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Organizations investing in AI-assisted search and knowledge retrieval are increasingly turning to Retrieval-Augmented Generation, commonly known as RAG, as a way to ground large language model outputs in their own internal data. The appeal is straightforward: instead of relying on a model’s pre-trained knowledge alone, RAG systems pull relevant documents from a curated knowledge base and use them to inform each generated response. The result, when built correctly, is a system that produces…
続きを読む>>10 Questions You Must Ask Before Hiring a RAG Implementation Consulting Firm
