RAG (Retrieval-Augmented Generation) connects an LLM to your approved knowledge sources so answers are grounded and traceable.
When RAG is a good fit
- your team needs answers from policies, contracts, manuals
- the content changes often
- you need citations and auditability
- permissions matter (teams shouldn’t see everything)
What you must design upfront
- source selection and freshness
- access control (RBAC)
- evaluation (correctness + citation quality)
- monitoring for drift and broken sources
If you want an assistant with permissions and citations, see RAG assistant.
Related:
- how to measure quality: RAG evaluation & metrics
- secure operation: AI security and access control
See proof from delivery in our case studies (e.g. MyZenCheck or Credizen).
For implementation in a real process with measurement, use AI implementation (30/60/90 days) or reach out via contact.