Add: Use AI to triage and act on reader feedback on your docs

learn/ai-for-docs/ai-triage-act-on-reader-feedback-docs.md

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+---
+seo:
+ title: Use AI to triage and act on reader feedback on your docs
+ description: How to sort, prioritize, and draft fixes for reader feedback on your docs with AI, then route the change through a normal pull request review.
+---
+
+# Use AI to triage and act on reader feedback on your docs
+
+Most doc teams collect reader feedback: a thumbs up or down, a star rating, a code snippet flagged as broken, sometimes a full comment explaining what went wrong. Few teams read all of it the same week it arrives, because a handful of writers cannot triage forty comments and still ship new content.
+
+That backlog is not a sign the feedback is unimportant. It is a sign nobody has a fast way to sort the useful reports from the noise.
+
+This article covers how to use AI to group, prioritize, and draft a first-pass fix for reader feedback, then hand the result to the same pull request review your docs already use for spec changes.
+
+## Where reader feedback comes from
+
+Reader feedback rarely arrives through one channel. A project can collect page-level "sentiment," rating, mood, scale, and comment [feedback forms](https://redocly.com/docs/realm/config/feedback) at the bottom of Markdown and API reference pages, plus a separate report button next to code snippets for readers who hit a broken example. Readers can also [leave a comment on the page](https://redocly.com/docs/end-user/interact-with-pages) after most form types, and logged-in readers' feedback carries an email automatically so a writer can follow up.
+
+All of it lands in one place: a project's [Feedback tab](https://redocly.com/docs/realm/reunite/project/feedback), split into Page feedback and Code reports, where each entry starts with a status of New and can be moved to Archived or Spam once someone has looked at it. That status field is built for triage, but somebody still has to read every row and decide what "New" should become.
+
+## Why raw feedback piles up untouched
+
+A rating without a comment tells you little on its own: a two-star page could mean confusing wording, a missing prerequisite, or a reader who wanted a feature the product does not have. A comment field mixes real bugs with one-off requests and the occasional typo report, so a writer opening the tab cold has to read every entry before knowing which ones are worth ten minutes of their day.
+
+Volume compounds the problem. A handful of comments a week is manageable by hand, but once a docs site gets meaningful traffic, the backlog grows faster than a small team can read it, and the reports that matter (a broken auth example, a missing endpoint) sit next to reports that do not (a reader who disagreed with a design choice).
+
+## Use AI to triage incoming feedback
+
+Export the current batch of feedback and code reports as CSV or JSON, then hand it to AI with a short prompt instead of asking someone to read every row cold.
+
+### Group similar comments before you read them one by one
+
+Ask AI to cluster the export by page and by theme: broken code samples, missing steps, terminology confusion, and everything else. Five readers describing the same broken `curl` example in five different ways should collapse into one item, not five separate tickets a writer re-reads independently.
+
+### Ask AI to separate the fixable from the unclear
+
+Within each cluster, ask AI to flag which comments point at something specific enough to act on and which ones need a follow-up question before anyone can fix anything.
+
+A vague entry like "this page is confusing" gives a writer nothing to act on. A triaged version, "the auth example on the OAuth page is missing the token refresh step a reader hit at the two-star rating," names the page, the missing step, and the reader's blocker. That second version is what a human should see first.
+
+## Turn a triaged item into a drafted fix
+
+Once AI has surfaced the items worth acting on, ask it to draft the fix instead of only describing the problem. Point AI at the page in question and the triaged comment, and ask for a proposed edit: an added prerequisite, a corrected code sample, a reworded step. [Use AI to accelerate and improve reviews](https://redocly.com/learn/ai-for-docs/ai-reviews) covers a related pattern for briefing AI with a short checklist rather than a long style guide; the same discipline applies here, since a focused prompt about one reader's blocker produces a more usable draft than an open-ended "fix this page" request.
+
+A useful prompt names the page, quotes the triaged comment, and states what "fixed" looks like, for example: "A reader on the OAuth page reported that the token refresh step is missing after the initial auth example. Draft a short paragraph and code sample that adds that step in the same style as the rest of the page." That level of detail gives AI one job instead of an open invitation to rewrite the page.
+
+Treat the draft as a starting point, not a merge-ready change. AI can misread which step a reader meant, or propose a fix that contradicts a decision the team already made on purpose, so a human still reads the draft against the original comment before it goes anywhere near a pull request.
+
+## Route the fix through review, not straight to production
+
+A drafted fix still needs the same checks any other documentation change gets. [Use AI to automate documentation reviews in your PR workflow](https://redocly.com/learn/ai-for-docs/ai-automate-documentation-reviews-pr-workflow) describes a three-layer pattern that fits here directly: an AI checklist pass on the diff, [Redocly CLI lint](https://redocly.com/docs/cli/commands/lint) against the spec when the fix touches an OpenAPI file, and a human reviewer who decides whether the change is right before it merges.
+
+Open the AI-drafted fix as a normal pull request and link back to the source feedback entry so the reviewer can see the original reader comment next to the proposed change. That link matters because a reviewer approving a fix in isolation cannot tell whether it actually addresses what the reader reported.
+
+## Close the loop with the reader
+
+Once the fix merges, update the feedback entry's status and, when the reader left an email, send a short note that the page changed because of their report. That last step is easy to skip and it is the one that turns a feedback form from a suggestion box nobody checks into a channel readers trust enough to keep using.
+
+Reader feedback and [AI usability testing](https://redocly.com/learn/ai-for-docs/ai-usability-testing) surface different problems: usability testing shows what a simulated reader hits on a task you chose, while page feedback shows whatever a real reader happened to notice. Running both means less goes unnoticed between the two.
+
+## How Redocly can help
+
+[Reunite](https://redocly.com/reunite) collects reader feedback directly on the page, through sentiment, rating, comment, and code-snippet report forms, and stores it in a Feedback tab you can triage without leaving the platform. Because Reunite is also where documentation changes move through Git branches, commits, and pull requests, the same surface that captures a reader's comment can carry the resulting fix through review, so triaging feedback and shipping the fix do not require switching tools.

learn/ai-for-docs/sidebars.yaml

@@ -16,6 +16,8 @@
   label: Use AI to review code examples for completeness and accuracy
 - page: ai-automate-documentation-reviews-pr-workflow.md
   label: Use AI to automate documentation reviews in your PR workflow
+- page: ai-triage-act-on-reader-feedback-docs.md
+  label: Use AI to triage and act on reader feedback on your docs
 - page: ai-review-docs-navigation-structure.md
   label: Use AI to review your docs navigation structure
 - page: ai-enforce-api-style-guide-at-scale.md