Best Practices for AI Code Reviews

AI can accelerate code reviews, but it does not replace engineering judgment. The best results come from combining AI speed with human accountability. This guide outlines a practical review workflow that keeps quality high without flooding teams with noisy comments.

1. Define what the AI should review

If you ask an AI to review everything, you will get a long list of generic comments. Instead, focus on specific risk areas:

• Security and data handling
• Error handling and edge cases
• Consistency with project conventions
• Performance hot spots
• Dependency and configuration changes

Make this list explicit. It becomes the review contract and reduces useless output.

2. Split reviews into passes

One review pass is rarely enough. Use two structured passes:

1. Safety pass: look for security issues, injection risks, unsafe file operations, or dangerous defaults.
2. Quality pass: look for readability, naming, test coverage, and design consistency.

Splitting the review makes the AI more focused and easier to validate.

3. Provide context, not just diffs

AI reviewers perform best with minimal but relevant context:

• The changed files
• The impacted module and its purpose
• A short description of expected behavior

Avoid dumping the entire repository unless necessary. Precision improves signal.

4. Require explicit evidence in comments

A good AI review comment should include:

• The file and line where the issue appears
• Why it is a problem
• A concrete suggestion

If the AI cannot point to a specific location or mechanism, treat the comment as speculative.

5. Use a standard severity rubric

Without a shared severity scale, teams ignore important issues and fix low-value ones. A simple rubric works well:

• Blocker: security, data loss, or production crash risk
• Major: correctness or performance issue
• Minor: readability or style improvement
• Nit: optional cleanup

Ask the AI to label comments with severity. This makes triage faster.

6. Guard against false positives

AI reviewers can hallucinate. To reduce this:

• Require code references in every comment.
• Ask for test evidence or reproduction steps.
• Reject comments that only paraphrase the diff.

If the AI claims a bug but cannot show the path, treat it as a prompt for human inspection, not a blocking issue.

7. Pair AI review with targeted tests

After the AI review, confirm the risk areas with tests:

• Add regression tests for reported edge cases
• Run existing unit and integration suites
• Confirm behavior with minimal fixtures

AI finds patterns, but tests confirm reality.

8. Keep the feedback volume small

If an AI posts 40 comments, engineers will ignore it. Limit output:

• Maximum 10 comments per PR
• Prioritize top risks
• Summarize remaining issues in a final note

High signal output builds trust; noisy output destroys it.

9. Use a review checklist

A lightweight checklist ensures consistent outcomes:

• Does the change break backwards compatibility?
• Are errors handled and surfaced clearly?
• Are permissions and access controls correct?
• Are tests updated for new behavior?
• Are docs or examples updated if needed?

Include the checklist in every AI review prompt to enforce discipline.

10. Keep a human in the loop

AI should never be the final authority. Use it to:

• Surface risks
• Suggest tests
• Highlight inconsistencies

But the merge decision stays with a human reviewer who understands the product context.

Example prompt structure

Here is a simple prompt layout that works well:

1. Project context and goal
2. Files changed and intent
3. Review focus areas
4. Output format and severity rubric

This produces more actionable comments and less generic filler.

Final thoughts

AI code review is most effective when it is bounded and verified. Define a clear scope, enforce severity labels, and require evidence for every comment. Treat the AI as a fast, systematic assistant, not a final judge. When you design the workflow this way, you get the best of both worlds: speed and reliability.