Retrieval-Augmented Generation (RAG)
Retrieving source material before generating an answer.
Technical Term
Full explanation
Retrieval-Augmented Generation, or RAG, is a technique where an AI system retrieves relevant information from an external source before generating a response, so the answer is grounded in current information rather than training data alone.
Why this matters
It is the reason publishing still works. If systems only used training data, nothing published today could affect an answer for years.
Where this sits
Retrieval-Augmented Generation (RAG) belongs to Retrieval, Evidence & Consensus, one of the seven layers of the Aingworth Glossary. Information exists, then it is retrieved, then sources are selected, then they corroborate one another, then consensus emerges. This is where Aingworth's Consensus Over Rankings thesis lives.
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