Retrieval-Augmented Generation (RAG) combines information retrieval with large language models (LLMs) so responses can be grounded in external documents, knowledge bases, databases, or enterprise content.
RAG is useful for technical documentation, support knowledge, policy search, research synthesis, and internal knowledge-management workflows because it can make AI output more traceable to source material.
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