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RAG is a technique that pairs a language model with a search step. Instead of asking the model to answer purely from what it memorized during training, you first retrieve the most relevant chunks of your own documents — policy PDFs, product manuals, past support tickets, internal wikis — and hand those chunks to the model along with the question. The model then answers using what it was just shown, not what it vaguely remembers.