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Keep AI-generated documentation accurate by treating it as a draft grounded in the current repository, updating it in the same review as the code change it describes, and checking it with both automation and a human reviewer. CI can catch broken links, stale generated files, or failing examples; it cannot establish that a sentence correctly describes what the software does.
Make documentation part of the code change
When a change alters an API, command, configuration default, workflow, installation step, deployment process, or other user-visible behavior, identify the documentation that explains it. Update the affected files in the same pull request or change list where practical. Google’s documentation guidance states, “Change your documentation in the same CL as the code change.” Google Documentation Best Practices also recommends keeping documentation current rather than accumulating a larger set of neglected pages.
For each change, ask the author and any AI assistant to name the docs and examples that may be affected. If no documentation update is needed, record why in the change review. That makes the decision visible rather than leaving readers to discover a mismatch later.
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Track likely source-to-document relationships
For repositories where it is manageable, make it easy to see which docs relate to which source areas—for example, through ownership information, links, or a concise map maintained with the project. This is an implementation choice, not a universal requirement. Its purpose is to help reviewers find the right docs when behavior changes.
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Give the AI current, project-specific evidence
Do not ask an assistant to reconstruct project behavior from general knowledge. Provide the relevant source files, schemas, tests, approved documentation, current version information, and the change under review. GitHub recommends supplying repository documentation and recent pull requests as context in its repository custom-instructions guidance.
Make the task verifiable: ask the assistant to identify which evidence supports each consequential claim and to flag missing information instead of guessing. GitLab’s guidance on AI-assisted code suggestions cautions against inventing command syntax, API parameters, or interface names. Verify those details against the project’s actual source and authoritative references.
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Keep intent separate from mechanically derived facts
When a fact is already represented in a schema or source definition, prefer generating its reference material from that structured source over maintaining two competing copies by hand. Explanations of why a design exists, what tradeoffs it makes, or how operators should use it still need a knowledgeable owner: those judgments are not reliably derivable from code alone.
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Use CI for repeatable checks that suit the repository. Depending on the project, that can include regenerating reference pages, checking that committed generated files match their sources, validating links and formatting, or building and testing documentation examples. GitLab describes a source-to-generated-output pattern in which CI regenerates selected AI instruction groups from their underlying principles: GitLab documentation for AI features and its instructions section.
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These checks reduce preventable maintenance errors, but they do not verify meaning. A page can pass every formatting and build check while describing an obsolete default or a command that now behaves differently. Treat a green CI result as evidence that mechanical conditions passed—not as approval of the prose.
Make examples testable where it is practical
If an example can be run or built safely, arrange for it to be checked using the project’s normal test or build process. A checked example is stronger evidence than a code block that merely looks plausible, but reviewers still need to consider whether it is representative and whether its surrounding explanation matches current behavior.
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Have a person review the claims and examples
A maintainer should read AI-assisted documentation as a project change, not as automatically trusted output. Check that the text matches actual behavior, names the right versions, and uses real commands, paths, parameters, defaults, and interface labels. Review examples for correctness and relevance, and check any security or permission boundaries they describe.
This is accountability, not just proofreading. The UK government’s guidance on using AI in the workplace says humans remain responsible for outcomes and should apply critical oversight to AI-generated or AI-assisted work. Microsoft’s responsible AI guidance for developers similarly advises developers to understand changes and thoroughly test AI-generated code. Documentation deserves the same scrutiny: committing it makes it part of the project’s instructions to users.
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Use this review checklist for each behavior change
- Have you identified whether the changed public behavior affects documentation or examples?
- Is the AI-assisted text grounded in the repository’s current code, tests, schemas, approved docs, and version?
- Do commands, paths, parameters, defaults, and interface names exist and behave as described?
- Can examples be executed or checked, and do they represent the intended use?
- Are generated files regenerated from their designated source rather than edited inconsistently?
- Were relevant docs updated in the same review, or is the reason for no update recorded?
- Has a human maintainer read and approved the factual claims?
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