RAG is a technique that retrieves relevant information from an external knowledge base before an AI model generates its answer, making answers more accurate and up to date than relying only on what the model learned in training.
A RAG system works in two steps: first it retrieves the documents or passages most relevant to the user's question, usually with vector search; then it uses those passages as context for the model to generate the final answer. It's the technique behind assistants that answer from a company's internal documentation, without retraining the model every time the content changes.
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