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Use AI code review as an extra pass over a pull request—not as proof that the change is correct or safe. Give the reviewer concrete project standards, check every finding against the latest diff, and rely on tests and human judgment for consequential decisions.
How to use AI to review a pull request
- Define the review scope. Describe the intended behavior, the affected components or boundaries, and the risks that matter for this change. Ask for specific checks rather than a vague request to “be more accurate.”
- Provide repository context. Put stable conventions and review criteria in the repository’s instructions. Include relevant coding standards, security checks, and readability preferences directly; GitHub says Copilot does not follow external links as a substitute for instructions. See GitHub’s guidance on repository custom instructions.
- Choose review depth for the change. GitHub describes Copilot’s Lite effort level as targeted feedback and Balanced as deeper analysis for complex logic, security-sensitive changes, and cross-service work. Check current settings and usage terms before relying on a particular mode.
- Inspect each finding. Read the cited lines and surrounding control flow. Reproduce or test the concern where practical, and confirm that a suggested change preserves the requirements. Treat comments as hypotheses: AI review can miss real problems and can flag problems that are not present.
- Validate independently. Run the project’s relevant checks and have a human reviewer assess consequential or security-sensitive changes. Do not infer approval or merge readiness from the existence of an AI review.
- Review the current diff again after changes. A new push may not trigger another review unless the relevant automatic-review setting is enabled. Request a fresh review when needed and check that comments still apply to the latest changes.
What to put in AI reviewer instructions
Useful instructions make the repository’s expectations explicit and reviewable. GitHub’s examples cover coding standards, review criteria, security checks, and readability preferences. Adapt a checklist to your project rather than assuming one template works everywhere.
- Behavior: State the expected outcome and important edge cases.
- Boundaries: Identify affected APIs, services, data flows, or compatibility requirements.
- Security: Name relevant concerns, such as authorization checks or handling of sensitive data.
- Project conventions: Specify the standards that apply to this codebase.
- Actionable findings: Ask for a concrete explanation of the risk and the relevant code location; avoid asking for vague improvements to “quality.”
Repository instructions help provide context, but they do not guarantee that a reviewer will catch every issue or follow every criterion.
GitHub Copilot review: settings and limitations to check
GitHub documents Copilot code review on GitHub.com and in several developer surfaces. Availability depends on plan eligibility and, in some environments, organization policy. Its product documentation also describes review effort levels and review settings; check the current Copilot code review documentation for the applicable setup.
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Comments are not the same as approval
Copilot’s default review is a “Comment,” not an “Approve” or “Request changes” review. Administrators can enable approval behavior, but GitHub identifies Copilot approvals as a public preview and subject to change. Confirm repository rules and required human approvals separately; an AI comment does not satisfy a required approval by default. See GitHub’s configuration documentation.
Review effort and estimated usage
GitHub lists estimated AI-credit ranges of $0.05–$1 for a Lite review and $0.25–$5 for a Balanced review. These are GitHub estimates per review, not a guaranteed charge; actual costs and billing rules can change. Check the current product and billing documentation before budgeting.
Some files may be excluded
GitHub’s documentation lists excluded file types, including dependency-management files such as package.json and Gemfile.lock, log files, and SVG files. Check the current limitations and exclusions, and use dedicated checks for files or risks the reviewer does not cover.
Re-reviews may repeat old comments
A new push does not necessarily cause Copilot to review the pull request again, and a repeated review can repeat earlier comments. Configure automatic reviews if appropriate or request a new one, then verify every comment against the current diff.
Can AI code review replace a human reviewer?
No. GitHub warns that Copilot is not guaranteed to spot every problem and advises users to validate its feedback carefully. AI review can be a useful additional check, but it does not establish that code is correct, secure, or ready to merge. Keep human review and project-specific tests or analysis in place, especially for changes with significant consequences.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare AI code-review options
Product behavior varies, so compare capabilities that affect your actual workflow rather than assuming results transfer between tools. Check where reviews run and which repository hosts or IDEs are supported; what context and repository instructions they can use; review depth and latency; plan, policy, and usage costs; how comments and approvals interact with merge rules; excluded files and limitations; and whether findings can be reproduced with tests or other analysis. GitHub’s documented support, effort levels, policies, costs, and exclusions describe Copilot specifically—not a comparative accuracy result across vendors.
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