# Knowledge Systems & RAG

> A retrieval-augmented generation (RAG) knowledge system answers questions by first retrieving the relevant passages from your own documents and then generating an answer grounded in them — with citations back to the source — so responses come from your knowledge rather than a model's guesswork.

_Source: https://plenaura.com/services/rag-knowledge-systems · Last updated: 2026-06-03 · Plenaura_

**Outcome:** The knowledge trapped across your documents, wikis, and drives becomes something people can just ask — with answers grounded in the real source and a citation to prove it.

## What you get

- A private assistant that answers from your documents, with citations to the source
- Access controls so each user only gets answers from what they're allowed to see
- Honest 'I don't have that' behavior instead of confident invented answers
- Connection to your real document sources — drives, wikis, ticketing, databases
- The system deployed on your infrastructure and owned by you — your data stays yours

## This is for you if

- Answers live buried in documents, wikis, and people's heads
- Staff or customers wait on someone to look things up
- You need answers that cite a trustworthy source, not a black box
- You want a private assistant that never leaks your documents to train someone else's model

## FAQ

### How is this different from just using ChatGPT?

A general chatbot answers from what it learned on the public internet and will confidently make things up about your business. A RAG system answers only from your documents, cites where each answer came from, and says 'I don't have that' when your documents don't cover it. It's your knowledge, grounded and private — not a model's best guess.

### How do you stop it from making things up?

By grounding every answer in retrieved passages from your documents and showing the citation, so an answer without a source is visibly an answer without a source. When retrieval finds nothing relevant, the system is built to say it doesn't know rather than inventing a plausible-sounding reply. Hallucination isn't eliminated by magic — it's constrained by design.

### Does it respect who's allowed to see what?

Yes. The system honors your existing access controls, so a user only ever gets answers drawn from documents they're permitted to see. Sensitive material doesn't leak into an answer for someone who shouldn't have it.

### Where does our data live, and who can see it?

On your infrastructure, under your control. Your documents are used to answer your questions — never to train a shared or third-party model. We can deploy on-premise or in your cloud account, and you own the whole system.