AI Code Reviews
Overview
AI code reviews use models to surface bugs, regressions, and missing checks before a human review.
Why It Matters
They can catch obvious issues faster and help reviewers focus on higher-value feedback.
Core Concepts
- AI finds candidate issues.
- Humans make the final judgment.
- Reviews should be anchored in the diff and surrounding code.
Mental Models
Use AI as a first-pass reviewer, not as a rubber stamp.
Best Practices
- Ask for specific bug and regression checks.
- Require human verification.
- Compare suggestions against the actual code.
Common Mistakes
- Accepting every suggestion.
- Asking for style feedback instead of risk review.
- Using AI review to skip understanding the diff.
Trade-offs
AI reviews can speed triage, but false positives and false confidence are real risks.
Decision Framework
| Review type | AI value |
|---|---|
| Bug scan | High |
| Style pass | Moderate |
| Final approval | Human only |
Examples
- “Find regressions, missing null checks, and test gaps in this diff.”
Checklists
- Did I verify the suggestions?
- Are the findings tied to real code?
- Did AI replace or assist judgment?
Senior Engineer Notes
Senior engineers use AI reviews to widen coverage, not to weaken the review bar.