AI coding assistants like GitHub Copilot are increasingly generating real code that ends up in pull requests. But just because a machine wrote it doesn't mean you can skip the review process. In fact, reviewing AI-generated code requires a different mindset than reviewing human-written contributions.

Start With the Basics: Open the Pull Request

Before diving into specifics, treat an AI-generated PR exactly like any other PR. Navigate to the repository on GitHub, locate the pull request, and give yourself adequate time to review it properly. Don't rush through a Copilot-authored change just because the diff looks clean at first glance. The real issues often hide in the implementation details that automated tools miss.

Read the Diff Line by Line

This is where your human judgment becomes irreplaceable. AI can generate syntactically correct code, but it doesn't always understand your codebase's conventions, architectural decisions, or business logic. When reading through each change, ask yourself: Does this implementation match our existing patterns? Are variable names descriptive and consistent with the rest of the codebase? Does the new logic actually solve the problem it's supposed to address?

Run the Automated Checks

GitHub Actions and other CI/CD pipelines should run automatically on every PR. Before approving, verify that all checks have passed successfully. Look beyond green checkmarks thoughβ€”examine test coverage reports, linting results, and any security scanning outputs. AI-generated code can sometimes pass basic tests while introducing subtle bugs or security vulnerabilities that require deeper inspection.

Treat Copilot as a Second Reviewer

Here's the mental shift that changes everything: view Copilot's suggestions as another developer's input rather than gospel truth. When you request changes on an AI PR, you're not criticizing a colleagueβ€”you're doing exactly what code review is designed for. Request clarifications, suggest alternative implementations, and don't hesitate to ask for rewrites when something doesn't feel right.

What to Watch For

AI-generated code has predictable failure modes that experienced developers learn to spot quickly. Be on the lookout for hardcoded values that should be configurable, missing error handling, edge cases that aren't addressed, and library imports that don't match your project's dependencies. Copilot often generates plausible-looking but incorrect solutions when it lacks sufficient context about what you're actually trying to build.

Key Takeaways

  • Always review AI-generated PRs with the same rigor as human contributions
  • Read diffs line by line rather than skimming for surface-level approval
  • Verify all automated checks passed and examine their detailed outputs
  • Treat Copilot as a helpful but imperfect collaborator, not an authority
  • Watch for hardcoded values, missing error handling, and edge case gaps

The Bottom Line

AI coding assistants are powerful tools, but they're still just tools. Your expertise, institutional knowledge, and judgment remain essential to maintaining code quality. Embrace the productivity gains while staying firm on standardsβ€”your future self (and your teammates) will thank you when bugs don't make it to production.