Private AI over your own documents: how local RAG works
You can ask questions of your policies, contracts and manuals without sending them to a public AI service. This is how retrieval-augmented generation does it.
Published · 5 min read
Many organisations want an assistant that answers from their own documents, but cannot put confidential files into a public chatbot. A model that runs on your own hardware, combined with retrieval, solves that.
What RAG means
Retrieval-augmented generation has two steps. First, the system searches your documents for the passages most relevant to the question. Second, a language model writes an answer using only those passages, and cites them. The model does not need to be trained on your files, so your documents are not baked into it.
Why host it locally
- Documents and questions never leave infrastructure you control.
- You decide who can query which documents, following the permissions you already have.
- Costs are predictable: hardware and maintenance rather than per-question fees.
What to expect, honestly
A locally hosted model is smaller than the largest public ones, so it is best at answering from the text in front of it, not at open-ended reasoning. Answer quality depends mostly on the quality of your documents and on how they are split and indexed.
Good systems show the source passage next to every answer so a person can verify it, and say so when the documents do not contain an answer. Treat the output as a fast first read, not as a decision.
Where it fits
Policy and procedure lookup, contract clause search, technical manuals, onboarding questions and internal knowledge bases are the usual starting points. Start with one document set, measure whether staff trust the answers, then widen.