A clean-looking diff is not proof that a change is correct. AI-generated code can be readable, well-commented, and still solve the wrong problem.
Review it with the same responsibility you would apply to any other contribution. The authoring tool may change how quickly code appears; it does not remove the need to understand what will run.
Restate the intended behavior
Read the task before the diff. Write a short description of what should change and what should remain unchanged.
This prevents the review from becoming a discussion of style while a missing business rule goes unnoticed. If the requirement itself is unclear, resolve that before approving implementation.
Look for scope expansion
Check new dependencies, configuration changes, database edits, and unrelated cleanup. Ask why each is necessary.
An apparently small interface fix should not include a broad authentication rewrite without a separate explanation. Work that affects API and backend behavior deserves explicit review of its boundaries.
Trace one real journey
Follow a normal input through the changed code to its visible result. Then trace an invalid input or a failed dependency.
Look for missing error handling, duplicated writes, and assumptions about data that may not hold. Keep the review tied to the application rather than searching for abstract perfection.
让这些想法落地。
从解决具体问题到建设完整网站，我们帮助您明确范围并完成实施。
聊聊我的网站定制网络和应用程序开发告诉我们您的目标。我们通常会在一个工作日内回复问题和具体的后续步骤。
Inspect the tests as code
Read assertions, fixtures, and mocks. A test that reproduces the implementation's mistaken assumption can pass while the product remains wrong.
Use the Claude Code verification guidance as background, then require evidence appropriate to your own project. Confirm which tests actually ran and which were skipped.
Leave an actionable review
Explain the condition that fails, why it matters, and what evidence would resolve it. Avoid vague comments such as “make this safer” when you can describe the missing check.
Our engineering prompting guide can help improve the next task brief. For a repeatable team process, explore AI integration services.
Approval should mean that a responsible reviewer understands the change and its remaining risks. It should not mean that the generated explanation sounded confident.
常见问题
Can another AI review the generated code?
It can provide an additional perspective, but it should not replace responsible human review and appropriate tests.
Should reviewers inspect generated tests?
Yes. Tests can contain the same wrong assumptions as the implementation and need their own review.
What if the code works but changes unrelated files?
Ask for the scope to be reduced or justified. Unrelated changes increase the work needed to understand and verify the patch.
