The fastest way to ruin trust in an AI documentation workflow is to let drafts publish without review. The model will get something wrong (a parameter name, a default value, a sentence that contradicts a nearby page), and the moment a user hits it, the credibility of every other AI-touched page drops. Recovering from that is harder than slowing the loop down by a day.
A review workflow that works treats AI output as a proposal, not a commit. The goal is to make the review fast enough that drafts don't pile up, but never invisible.
The two-stage model
Most teams converge on the same shape:
- Draft. The AI proposes changes on a branch. The diff is visible side by side with the original page.
- Review. A human approves, edits in place, or rejects.
- Merge. Approved changes go live.
The branch is doing real work. It keeps proposed changes off the live docs, lets reviewers see the diff, and makes it easy to throw away a draft that isn't worth fixing.
Who reviews
There's no universal answer. A few patterns that work:
- The PR author reviews their own doc draft. Fast, low overhead. Works when engineers are comfortable making the final call on docs and the docs are mostly reference material.
- A technical writer reviews. Higher quality, slower. Works when the docs need a consistent voice and the writer has the bandwidth.
- Hybrid: writer reviews narrative, engineer reviews reference. Most teams end up here. The writer catches voice and structure, the engineer catches technical mistakes.
What doesn't work is "anyone can review." Drafts pile up because nobody owns them.
Review latency matters more than review depth
A perfect review that takes a week is worse than a quick review that takes an hour. The reason has to do with the rest of the workflow. If the code change shipped last Tuesday and the docs are still in draft on Friday, the next code change is already on top of the first one. Drafts that sit get stale, then get harder to review, then get abandoned.
A reasonable target is review within one business day of the draft landing. Five minutes per draft is plenty for most changes. If a draft needs more than fifteen minutes, you're usually better off rewriting it by hand than continuing to edit the AI version.
What reviewers should look for
A short checklist that fits most reviews:
- Did the AI identify the right page? Sometimes the most important fix is moving the proposed change to a different doc.
- Is every fact in the draft supported by the diff or the existing docs? Watch for confidently-stated details the model invented.
- Does the voice match? A docs-grounded model usually gets this right, but not always.
- Are there missing updates? If the diff renamed a parameter, every page that mentions the old name should be updated. The AI may have only caught one.
The last item is the most-skipped check. A model that proposes one change can miss the rippling effects of that change elsewhere on the site.
Closing the loop with engineering
If the AI writer is triggered by pull requests, the review loop should be visible to engineering. A comment on the PR with a link to the docs preview means the engineer who shipped the change can see the proposed update without leaving GitHub. They're also the best source for "this draft is wrong, here is what actually changed."
That feedback path is what makes the difference between a docs team that owns the AI workflow and a docs team that fights it. Engineering sees the proposal, can flag mistakes, and gets visibility into what their work means for users.
What to skip
A few review behaviors that look helpful and aren't:
- Wordsmithing AI output to perfection. If the draft is mostly right, ship it. Quality compounds over many small improvements, not one polished page.
- Adding new content that wasn't in the diff. Save that for a separate update. Scope creep makes reviews slow and risky.
- Reviewing every line for style. A linter is faster and more consistent than a human at this. Use the Linter to enforce rules and reserve human attention for judgment calls.
The review process is the throttle on the workflow. Get it right and AI drafts become a force multiplier. Get it wrong and the drafts stop, the team stops trusting them, and the docs go back to drifting.
