OKF validator
OKF validator checks that make AI answers more reliable
Validation catches missing titles, weak summaries, broken structure, and metadata gaps before your knowledge reaches an AI agent.
Bad structure becomes bad retrieval
AI answer quality depends heavily on what gets retrieved. If a knowledge file has no title, no description, weak headings, or unclear scope, the retrieval layer has to guess.
An OKF validator gives teams a repeatable quality gate before content gets indexed. That matters when the content feeds support agents, customer bots, engineering copilots, or internal search.
The checks that matter first
Start with metadata: required type, title, slug, description, source format, tags, and timestamp. Then check the body for headings, task sections, examples, links, and clear boundaries.
The goal is not bureaucracy. The goal is to catch the missing context that causes an AI system to retrieve the wrong chunk or answer with confidence when it should ask for more detail.
Validation should run everywhere
The best workflow runs validation in the browser for quick fixes and in CI for production knowledge repos.
That gives writers fast feedback while protecting the indexed knowledge base from broken files, weak metadata, and accidental structure drift.