What Is RAG (Retrieval-Augmented Generation)?
Retrieval-augmented generation (RAG) is a technique in which an AI system first searches a set of documents for information relevant to a question, then gives that information to a language model to generate its answer. It helps answers stay grounded in specific, up-to-date sources.
How RAG Works
Documents are split into passages and converted into embeddings. When a question arrives, the most similar passages are retrieved and added to the prompt, and the model answers using them.
Why It Is Used
RAG can reduce hallucinations, lets answers cite sources, and allows information to be updated without retraining the model.
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🔗 Related Terms
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- API (Application Programming Interface)
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Last updated: September 29, 2026. This definition is for general educational purposes only. It is not financial, investment, tax, legal, or medical advice, and it does not recommend any product or course of action. For decisions about your own situation, consult a qualified professional.