RAG is a technique that combines a large language model (LLM) with a vector store of embedded text chunks to retrieve relevant context at query time. In practice you feed a user prompt to the LLM, first pull the most relevant passages from an external knowledge base, then let the model generate a response that is grounded in those passages. This matters because it dramatically reduces hallucinations, lets you keep the model's knowledge up-to-date without costly fine-tuning, and lets you answer domain-specific questions with a single API call.
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