Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation (RAG) is a technique where an AI model retrieves relevant documents from a knowledge source and uses them as context to ground its response.

RAG lets a model answer from up-to-date or proprietary data it was not trained on, and reduces hallucination by anchoring output to retrieved evidence.

It is a common pattern in support and research agents, where answers must reflect a specific knowledge base.