Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) is how you make an AI assistant trustworthy about your business. Instead of letting the model answer from its training memory, the system first retrieves the relevant passages from your approved documents, hands them to the model, and instructs it to answer only from those passages — citing them.

The quality of a RAG system is mostly the quality of its library: documents must be approved, current, and access-controlled. That is why this plan treats knowledge management — upload, review, approve, publish — as a governed workflow, not a folder dump.

Where it shows up in this proposal: the knowledge assistant in the Recommended First Pilot is a RAG system over approved documents, running on local infrastructure.