Review an AI-generated pull request as you would any proposed code change: verify that it solves the intended problem, inspect the full diff, run the relevant checks, and merge only after the repository’s required approvals and safeguards pass. The reviewer—not the AI summary or review bot—owns the merge decision.
Start with the request, not the AI summary
Read the issue or task, the pull request description, and any linked discussion before judging the implementation. Establish what the change is meant to do, then check whether it fits the project’s architecture and conventions. GitHub recommends giving reviewers context about why a change is needed, what changed, and where attention is most useful: Helping others review your changes.
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Use a generated summary to find your way around the change, not as proof that the summary is complete or correct. Read the diff yourself and compare the implementation with the original request.
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Inspect the complete diff for scope and risk
Review all changed files, not only the main source file. Check configuration, generated output, dependency manifests, tests, and workflow files for changes that are unrelated, unexpected, or broader than the task requires. GitHub notes that “Small, focused pull requests are easier to review and safer to merge.”
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Give closer attention to changes involving authentication, permissions, CI workflows, dependencies, or sensitive data. A small-looking edit in one of these areas can alter what the application or automation is allowed to do. GitHub’s guidance recommends reviewing changes in context and examining relevant security findings: About code scanning alerts.
Verify behavior with tests and analysis
Run the project’s relevant tests and build, along with its configured static analysis. Check for failures, new warnings, missing coverage for the changed behavior, and edge cases such as invalid input or error paths. GitHub’s guidance for reviewing AI-generated code recommends using automated tests and static analysis; plausible-looking code is not evidence that it behaves correctly: Reviewing AI-generated code.
Choose checks based on what changed. A UI edit may need focused interaction tests; a data or API change may need validation of boundary conditions and error handling. If you cannot run a required check locally, rely on the repository’s configured check only after confirming it completed successfully for the current commit.
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Check every new dependency
For each added or changed package, verify that it exists, comes from a credible source, is maintained, and has a license compatible with the project. AI-generated code can suggest package names that do not exist; GitHub also warns about “slopsquatting,” in which attackers publish packages with names likely to be hallucinated by AI systems. Do not install a suggested package merely because its name appears plausible. See GitHub’s AI-generated code review guidance for its recommendations.
Resolve review feedback and rerun checks
Before making a change in response to a comment, understand the issue it identifies. Reproduce a reported problem where practical, make the fix, and rerun the checks affected by that fix. If the pull request changes substantially after review, request review again so reviewers assess the updated code rather than relying on an earlier pass. GitHub explains how to handle review feedback and resolve conversations in its guide to resolving pull request reviews.
Confirm the repository’s merge requirements
Before merging, check the requirements configured for the target branch: required approvals, code-owner review where applicable, required status checks, and any security-analysis rules. A pull request that looks correct—or has a green-looking summary—does not by itself authorize a merge. GitHub branch protection and rulesets can enforce different combinations of these controls, depending on repository settings: About protected branches.
Code-scanning merge protection can be configured to block specified findings or incomplete analysis, but availability and behavior depend on GitHub plan and workflow configuration. Treat scan results as findings to investigate, and verify that the required analysis actually ran for the pull request: Setting code scanning merge protection.
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Copilot code review may suggest issues worth investigating, but its comments still need human evaluation. A generated approval assessment by itself does not satisfy merge requirements. GitHub documents configurable approval behavior and identifies Copilot approvals as public preview, so check the current product settings before depending on them: Using GitHub Copilot code review.
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This workflow reflects GitHub’s documentation. Availability and configuration vary across GitHub repositories and plans; these instructions do not establish that equivalent controls exist on GitLab, Bitbucket, or self-hosted platforms.
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