The problem this solves
Documentation is the content AI assistants most want to cite for how-to and what-is questions: authoritative, specific, maintained. Most docs squander that advantage structurally. Answers are buried under preamble, procedures run as paragraphs instead of lists, definitions never appear crisply, and schema markup is absent - so engines quote a third-party tutorial that explains your own product less accurately than you do. The fix is well understood and perpetually deprioritized, because docs teams are staffed for correctness and coverage.
The stakes rise as more product questions get asked away from your site. When an assistant answers a how-do-I question about your product from someone else's writeup, you lose accuracy, context, and the link - and support inherits tickets that good extraction would have prevented. Restructuring the ten pages that answer the most questions captures most of the value for a fraction of the effort, provided the changes actually ship.
How the mission runs
- Pick the ten pages. Site Audit crawls the docs and identifies the pages doing the most answering work - the top how-to and definitional pages by traffic and internal linking - and AI Visibility checks which product questions engines currently answer from other people's content. The overlap becomes the target list.
- Grade extractability. Each target page is graded against extraction mechanics: does the definition lead, are procedures numbered lists, do headings match real question phrasings, is schema present, are dates visible. The grade sheet becomes the work order for the rewrite pass that follows.
- Restructure the content. The agent reworks each page in place: answer-first openings, procedures as clean steps, headings phrased as questions where natural, HowTo and FAQ schema added, dates surfaced. Technical accuracy is preserved; the changes are structural, and anything ambiguous is flagged for the docs owner.
- Keep diffs reviewable. Changes are committed page by page with clear messages, so a docs maintainer can review each rework in isolation. Nothing is reorganized beyond the target pages, and no page's meaning changes without a flag in the pull request description calling it out.
- Open the pull request. Everything lands as one GitHub pull request: ten reworked pages, schema additions, and a description mapping every change to the extractability grade it fixes. Your docs team reviews, adjusts, and merges through the normal pipeline - the agent never touches the default branch.
The prompt
This is the exact objective the agent receives. Swap the obvious placeholders for your own domain, segment or channel and run it as-is from the console, Slack, or the API.
What comes back
A GitHub pull request reworking your ten highest-value docs pages for answer extraction - definitions up front, steps as lists, question-phrased headings, HowTo and FAQ schema, visible dates - with a per-page explanation tying each change to the grade it fixes, plus the before-state grades so improvement is measurable. Merging stays with your team, and the pages ship through your existing docs deploy.
Make it yours
- Point the mission at your getting-started and troubleshooting sections specifically, since extraction failures on those pages are the ones that most directly turn into support tickets.
- Run it as grade-only first to see the extractability scores across the whole docs site before committing to the ten-page rework.
- Extend the target list to twenty pages across two sequential pull requests when the docs are large and the first merge went smoothly.
Frequently asked questions
Will the rewrite change what the docs say?
The mission's rule is structural change with preserved meaning: reordering, listifying, adding markup and dates. Where restructuring would alter a technical claim, the agent flags it in the PR for the docs owner to resolve. Review exists precisely to catch anything that reads differently.
How were these specific pages chosen?
From evidence in the run: crawl data showing which pages carry the most answering load, and live AI answers showing where engines bypass your docs for third-party sources. The PR description includes the selection reasoning, and you can override the list before the rework starts.
How will I know the rework worked?
Re-run the extractability grades after merging and query the same product questions through AI Visibility over the following weeks. The before-grades ship with the PR, so improvement is measurable on both layers: pages that grade higher, and answers that start citing your docs.