What is Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation grounds a language model's output in external evidence by retrieving relevant documents or data before generating a response, rather than relying purely on what the model memorized during training.

• A retrieval step searches an external knowledge source, often a vector database, for content relevant to the query
• That retrieved content is added to the model's context before it generates an answer
• Reduces hallucination by anchoring answers in retrievable, verifiable source material

RAG treats retrieval as a first-class operation feeding into generation, which is also why it's often described as a specific case of tool use, retrieval being just another tool an agent can call.

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