Retrieval is not an AI source of truth.
Wiki Management for AI Applications decides which sources are reviewed, which collections each application may use, and when an answer must refuse.
An AI source of truth is verified, permissioned knowledge with a policy per application. Wiki Management for AI Applications is that layer, not a second copy of your docs.
Most teams have storage. They call it a source of truth.
A folder of PDFs is storage. A vector index is retrieval. Neither one is an AI source of truth. A source of truth has an owner, a review state, a collection boundary, and a test for the question that would hurt you.
Wiki management for AI is the missing layer. Traditional wikis store pages for people. RAG tools retrieve chunks. Observability tools score outputs. None of those decide whether a public chatbot may see an internal page.
This category page ties the essays to the live product. If you have already read the long reads, this is the product-shaped version of the same argument.
What an AI source of truth actually requires
Five gates. Skip one and you are back to a helpful model with an unreviewed index.
- Step 1
Own the source
Every document that an AI may cite needs a person who can say whether it is still true.
- Step 2
Verify before retrieval
Draft and stale pages stay out of the application until someone reviews them.
- Step 3
Split public-safe and internal collections
The source of truth is not one pile. It is the right pile for each audience.
- Step 4
Policy per AI Application
A support chatbot, an internal copilot, and a help center do not share one unscoped index.
- Step 5
Prove the answer
Use the Answer Playground and Wiki Connect so the high-risk questions cite a source or refuse.
How Wiki implements wiki management for AI applications
The live product is verified sources, collection visibility, permission-aware retrieval, the Answer Playground, Wiki Connect, Knowledge Health, and public docs.
Verified sources and collections
Import URLs or files, assign owners, and review before AI use. Public-safe and internal visibility keep surfaces apart.
AI Application policies
Permission-aware retrieval applies the allow-list at answer time, not as a suggestion in a prompt.
Answer Playground and Wiki Connect
Test first. Then let your own bot call the HTTP API for citation-backed, policy-constrained answers.
Knowledge Health and public docs
See whether the workspace is ready, then publish the same approved knowledge as a help center.
Questions teams ask first.
- What is an AI source of truth?
- A governed set of verified sources, split into public-safe and internal collections, with a policy for each AI Application and a way to test or refuse answers. Retrieval alone is not that.
- What is wiki management for AI applications?
- The layer that decides what AI can use, who can see it, whether it is current enough to cite, and whether an answer can be trusted. That is the category Wiki is built for.
- How is this different from a knowledge base or RAG?
- A knowledge base stores content. RAG retrieves it. Wiki verifies sources, permissions them per application, tests answers, and publishes public docs from the same approved set.
- Where should I start reading?
- Start with What Is Wiki Management for AI Applications, then RAG Is Not a Source of Truth. This page is the product-shaped version of those essays.
Essays, tools, and the product.
From the blog
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.
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.
The Complete Guide to Citation-Backed AI Support
Customer-facing AI should show where the answer came from, know when it lacks an approved source, and escalate when documentation cannot support a reliable answer.
AI Permissions: What Your Chatbot Should Never Be Allowed to Know
The biggest AI knowledge mistake is giving every chatbot access to every document. Permissions should be explicit, tested, and audience-aware.
Tools
Your AI is already talking.
Make sure it knows what it is talking about.