A general-purpose AI model knows a lot about the world and nothing about your business. Retrieval-augmented generation, or RAG, closes that gap by letting the assistant look things up in your own material before it answers.
How it works
- Your documents are split into small passages and indexed
- When someone asks a question, the most relevant passages are retrieved
- The AI model writes an answer using those passages as its source
- The answer can point back to the passages it used
What it needs to work well
Quality depends mostly on the source material. Out-of-date, duplicated or contradictory documents produce confusing answers. Clear ownership of the content, and a plan for keeping it current, matter as much as the model.
RAG reduces made-up answers but does not remove them. For anything important, show the sources and keep a person in the loop.
Privacy questions to settle early
Decide which documents may be used, who can see which answers, and what is sent to an external AI provider. These choices shape the architecture, so they are best made before building.
Related service: AI & Intelligent Automation
AI assistants, document processing and workflow automation integrated with the systems you already use.
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