A RAG lets an LLM answer from YOUR documents, with sources — the safest way to get an internal assistant that doesn't make things up. The keys: indexing quality, data freshness, access control, and verifiable sourced answers.
What a RAG is (and why not a bare LLM)
RAG = Retrieval-Augmented Generation: relevant passages are retrieved from your content, then the LLM answers from them and cites sources. A bare LLM answers from memory and may hallucinate; RAG anchors it in your real data.
The pitfalls
Hallucination: neutralize with sourced answers and an explicit 'I don't know'.
Freshness: re-index when documents change.
Access rights: a user must only see what they're allowed to.
Indexing quality: careful chunking and metadata = good answers.
Production checklist
Reliable, up-to-date sources · cited answers · 'I don't know' guardrail · role-based access · evaluation on real questions · logging for continuous improvement.
Privacy
RAG runs on YOUR data; with a controlled architecture and zero-retention providers, your content isn't used to train third-party models.
Which risk → how to control it
| Risk | Mitigation | Result |
|---|---|---|
| Hallucination | Sourced answers + 'I don't know' | Reliable, verifiable answers |
| Stale data | Re-index on update | Always current |
| Data leak | Role-based access | Everyone sees only what they should |
| Wrong answer | Citations + evaluation | Trust and continuous improvement |
FAQ
RAG or fine-tuning?
RAG first: simpler, always up to date, cheaper, and it cites sources. Fine-tuning is only worth it in specific cases.
Which documents does it work on?
Contracts, procedures, knowledge bases, tickets, product docs — any structured or unstructured text corpus.
How long to set up?
A usable pilot RAG ships in a few weeks depending on volume and source cleanliness.
Is my data private?
Yes with a controlled architecture (RAG over your data, zero-retention providers). Your content doesn't train third-party models.
How do you avoid hallucinations?
Answers strictly grounded in retrieved sources, citations shown, and an 'I don't know' when the info isn't in the corpus.
An AI use case in mind? Erwan André (DirtyLab) frames it, builds it and ships it to production.