RAG (Retrieval-Augmented Generation)
A technique where an AI system retrieves relevant information from external sources before generating a response, grounding its output in real data rather than model memory alone.
Also known as: Retrieval-Augmented Generation, retrieval-augmented AI
Retrieval-Augmented Generation (RAG) is an AI architecture that combines information retrieval with language model generation. Instead of relying solely on what a model learned during training, a RAG system retrieves relevant documents, passages, or data points from an external knowledge base and uses them as context when generating a response. This allows the model to answer questions based on current information, specific knowledge bases, or proprietary data. For websites, RAG relevance means that your content may be retrieved and used as source material in AI-generated answers ... making structured, accurate, well-sourced content more valuable than ever.