RAG needs a governance layer.
Retrieval only sees verified, permissioned collections. Answers cite or refuse. Source changes trigger review before customer-facing AI updates.
RAG retrieves. Wiki governs whether retrieved documents are verified, allowed, and safe to cite for each AI application.
Unreviewed indexes become confident liars.
Teams ship RAG on a dump of Confluence and call it production. The index includes drafts, expired policies, and internal pages.
The model retrieves something close and answers with confidence. Retrieval worked. Governance never happened.
RAG governance means verification, collection boundaries, answer tests, and change control around the index.
What RAG governance looks like in practice
Keep retrieval. Add readiness gates.
- Step 1
Verify before index eligibility
Draft and stale pages do not become retrieval candidates.
- Step 2
Split collections by audience
Public bots and internal copilots do not share one unscoped corpus.
- Step 3
Test answers before launch
Playground cases catch missing citations and wrong-source retrieval.
- Step 4
Watch source changes
When a source moves, review before customer-facing AI reuses it.
How Wiki governs RAG-shaped systems
Wiki sits above retrieval as the readiness and policy layer.
Verification and ownership
Permission-aware policies
Releases and maintenance inbox
Questions teams ask first.
- Does Wiki replace my vector database?
- No. Wiki governs which knowledge is eligible and how answers must behave. Keep your retrieval stack if you want.
- Is RAG enough for production chatbots?
- Retrieval alone is not enough when documents are stale, conflicting, or internal. You need verification and permissions.
- Where should I start?
- Read Wiki vs RAG, then import one public policy, verify it, and test the refund question in the Playground.
Essays, tools, and the product.
From the blog
RAG Is Not a Source of Truth
Retrieval can find context. It does not decide whether that context is current, approved, allowed for the audience, or safe to cite.
How to Build an AI Knowledge Base for Production
A production AI knowledge base is governed, verified, permissioned, and test-covered. This guide walks through the six gates and a 30-day rollout.
What Is Wiki Management for AI Applications?
Wiki Management for AI Applications governs what AI can use, who can see it, whether it is current, and whether an answer can be trusted.
Tools
Your AI is already talking.
Make sure it knows what it is talking about.