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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 clear project-specific criteria, check every finding against the current diff, and have a human validate consequential or security-sensitive changes.
How to use AI to review a pull request
- Define the review scope. State the intended behavior, affected boundaries, and risks that matter for this change. Replace vague requests such as “be more accurate” with criteria a reviewer can check.
- Provide project context. Put stable coding standards and review criteria in repository instructions where the tool supports them. Include important requirements directly: GitHub says Copilot custom instructions cannot make the reviewer follow external links.
- Choose review depth for the change. Use targeted feedback for a straightforward change; consider deeper analysis for complex logic, security-sensitive code, or changes that cross services.
- Read each finding as a hypothesis. Inspect the cited lines and surrounding control flow. Reproduce or test the concern when practical, and check that any suggested fix preserves the intended behavior.
- Validate the change independently. Run the project’s appropriate checks and ask a human reviewer to assess important findings, especially for security-sensitive changes. Do not treat an AI review as approval or evidence of merge readiness.
- Review the latest diff again after changes. Request a fresh review when needed, then confirm every comment still applies to the current changes.
What to put in reviewer instructions
Useful instructions turn project expectations into reviewable checks. GitHub’s examples cover coding standards, review criteria, security checks, and readability preferences. Adapt that pattern to the repository rather than asking for an undefined “thorough” review.
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- Expected behavior: Describe what the change must do and any behavior it must preserve.
- Boundaries: Identify affected components, APIs, services, or data flows that deserve attention.
- Security criteria: Name relevant risks or checks for this change, such as authorization boundaries or handling of sensitive data.
- Project conventions: State applicable style, error-handling, testing, and readability standards.
- Review focus: Ask for actionable findings tied to specific code, and distinguish correctness or security concerns from optional style suggestions.
Instructions can guide attention, but they do not guarantee that the reviewer will catch a problem or follow every criterion.
GitHub Copilot code review: settings and limits
GitHub documents Copilot code review for GitHub.com and several developer surfaces, with eligibility depending on plan and, in some environments, organization policy. Check the current product documentation and repository or organization settings before relying on a feature.
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Comments are not approvals by default
GitHub’s default Copilot review is a “Comment,” not an “Approve” or “Request changes” review. Administrators can enable approval behavior; GitHub describes Copilot approvals as a public preview that may change. A comment therefore should not be treated as satisfying a required human approval or merge gate.
Effort level and estimated usage
GitHub describes Lite as targeted feedback and Balanced as deeper analysis for complex logic, security-sensitive changes, and cross-service changes. Its documentation gives estimated AI-credit ranges of $0.05–$1 per Lite review and $0.25–$5 per Balanced review. These are GitHub estimates, not guaranteed charges; actual billing depends on current usage rules and settings, which can change.
Re-reviews and changing diffs
A new push does not necessarily trigger another review. GitHub documents that another review requires the relevant automatic-review setting or a new request; repeated reviews can also repeat earlier comments. Check whether findings refer to the latest diff before acting on them.
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Excluded files
GitHub lists exclusions for Copilot code review that include dependency-management files such as package.json and Gemfile.lock, log files, and SVG files. Check the current exclusion list and use appropriate dedicated analysis for files or risks the assistant does not cover.
Why human verification still matters
AI review can miss real problems and can flag problems that are not present. GitHub Docs cautions that “Copilot is not guaranteed to spot all problems or issues in a pull request” and says to “Always validate Copilot’s feedback carefully.” A useful finding should lead to inspection, testing, or another verification—not automatic acceptance.
For a consequential finding, trace the relevant code path and test the behavior where practical. For a suggested change, verify that it fixes the identified issue without breaking the requirement or introducing a new one. Keep normal human review and project checks in place.
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How to compare AI code-review options
There is no general accuracy percentage established here that can rank AI review products. Compare concrete workflow capabilities instead:
- Where reviews run: repository hosts, pull-request workflows, and supported IDEs.
- What context the reviewer can use, including repository instructions and project conventions.
- Available review depth and expected latency.
- Plan eligibility, organization policies, and usage costs.
- Whether comments can approve a change and how they interact with required reviews and merge rules.
- Excluded files and documented limitations.
- Whether findings can be reproduced with tests or other analysis.
These criteria help assess fit; they are not a comparative product test. Feature support, pricing, policies, and preview status can change.
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Official references
- GitHub Docs: Using Copilot code review
- GitHub Docs: Configuring Copilot code review
- GitHub Docs: Customizing Copilot code review
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