Retrieval-Augmented Generation

Most AI models answer based on what they learned during training, which means they have no access to your internal policies, contracts, product documentation, or anything updated after their training cutoff. Retrieval-augmented generation (RAG) solves this by adding a step: before generating a response, the system searches a document index for relevant content and uses what it finds to inform the answer. The result is a system that can say "*according to your Q3 vendor policy...*" rather than guessing or refusing.

RAG is the architecture behind most "AI on our data" proposals, so executives will encounter it whether or not it's named. The business value is real: faster policy lookups, better-informed support agents, contract research that used to take hours. But the risks are specific: a RAG system is only as good as its document index, and it can still misrepresent what it retrieves. Leaders need to know who owns the index, how it stays current, and what the system does when it can't find a supported answer, as those are operational decisions, not technical ones.