Guide

AI Documentation Review

A documentation review guide for checking AI-written docs against source behavior, examples, links, version notes, and reviewer evidence before publishing.

Review AI-generated docs against the product, not against the model’s explanation. A model can produce a confident page that describes intended behavior, old behavior, or invented options. Documentation review must therefore compare claims to source behavior, examples, links, screenshots, version notes, and user tasks.

AI can help draft and reorganize docs, but the reviewer owns accuracy. The documentation assistant workflow shows how to create a source-backed draft; this page focuses on the human review gate before publication.

The problem with AI-written docs

Documentation failures are expensive because users act on them. A wrong flag can waste hours. A stale screenshot can send users to the wrong setting. An invented API option can create support load. A missing caveat can make a safe operation risky.

AI-written docs are especially risky when they sound polished. Reviewers may read for style and miss factual drift. The review process should force a claim-by-claim check against source evidence.

What to review

Check product behavior. Does the doc match the current product, API, CLI, or workflow? If behavior varies by plan, version, environment, or permission, the page should say so.

Check examples. Code snippets, commands, configuration, request bodies, and screenshots should be tested or marked as unverified. Do not publish examples that only look plausible.

Check labels and names. API fields, buttons, menu labels, command flags, file paths, environment variables, and error messages should match the product exactly.

Check links. Internal links should resolve and point to the right canonical page. External links should be necessary and current. If a source is used for a claim, the claim should not exceed that source.

Check audience fit. A page for beginners should not skip prerequisites. A reference page should not bury exact parameters under narrative.

Step-by-step method

First, ask the AI system or writer to provide a claim table. Each important statement should map to a source: code, API contract, test, release note, issue, product screenshot, or approved policy.

Second, inspect changes as a diff. AI rewrites can remove useful warnings or examples while improving style. Review what changed, not only the final page.

Third, test examples in an approved environment when possible. If local validation is not available, record that limitation and require a maintainer review.

Fourth, check version and release readiness. Docs should not describe future behavior unless clearly labeled as roadmap or preview. If the product has not shipped, the page should not imply that it has.

Fifth, review the final page for task flow. Users should be able to complete the job without jumping across unnecessary sections.

Sixth, check what the AI removed. Documentation regressions often come from omitted warnings, edge cases, or troubleshooting details rather than from newly added false claims. Compare the previous version and make sure useful context was not smoothed away.

Verification gate

Do not publish until the reviewer can answer: Which source supports each behavior claim? Which examples were tested? Which links were checked? Which caveats remain? Which unresolved questions are assigned?

Use the AI summary verification guide when the documentation includes summarized release notes or source material. Use source-backed AI writing for claim discipline.

Human review point

A maintainer, product owner, or support owner should approve docs before publication when the page affects customer behavior. The review packet should include the source table, diff, validation evidence, and open questions. The human-in-the-loop AI workflows guide helps make the handoff explicit.

Failure modes

AI documentation review fails when reviewers check grammar but not source behavior, when examples are not run, when old docs are paraphrased into new wording, or when future behavior is published as current. It also fails when the reviewer cannot see what evidence the model used.

It can also fail when release pressure changes the standard. If the page is not ready, publish a smaller verified update instead of a broad rewrite with untested examples.

Frequently asked questions

What should reviewers check in AI-written documentation?

Reviewers should check source behavior, examples, commands, flags, links, screenshots, version notes, limits, caveats, and user-facing clarity.

What is the biggest risk in AI documentation?

The biggest risk is fluent documentation that describes intended, obsolete, or invented behavior instead of the product users actually have.

Next step

Before accepting an AI documentation draft, require a claim table and a changed-section diff. If either is missing, treat the draft as unverified.