Retrieval-Augmented Generation (RAG) Architecture

Definition

A technical architecture that enhances large language model outputs by retrieving relevant information from an external knowledge base before generating a response, grounding the model's output in verified, up-to-date, and domain-specific data. RAG reduces hallucination risk, enables LLMs to access proprietary or recent information not in their training data, and provides citation capabilities. RAG architectures are a key component of enterprise AI deployments and create significant value from the combination of proprietary knowledge bases with general-purpose language models.