Retrieval-Augmented Generation (RAG)

A technique that enhances AI model responses by retrieving relevant information from external knowledge sources before generating a response. RAG systems typically use vector databases to store and retrieve document chunks based on semantic similarity. This approach reduces hallucination and enables agents to provide up-to-date information beyond their training data.