AI Drafted the Docs. Your Job Is Decisions, Not Prose.
When a language model drafts documentation, the bottleneck shifts from writing to reviewing, and most review habits were built for scarce text. Teams respond by reading generated prose line by line, which spends attention on wording the model can regenerate in seconds. The better split separates labor by kind rather than by time: the model produces candidates, a script extracts decision points, and the human answers those decisions. This article shows a review-brief pipeline that turns every generated section into a small list of ownership decisions a merge gate can enforce.
Why line-by-line review fails
Documentation review traditionally assumes that text is scarce enough to justify close reading. Generated text breaks that assumption, because the cost of regenerating a paragraph approaches zero while the cost of verifying a claim stays constant. Two failure patterns appear in teams that review AI drafts the old way. The first is volume acceptance, where long documents get merged because the act of reading felt like work. The second is review theater, where humans edit punctuation while wrong parameter names survive into production.
