Guide

AI Code Review Prompts

Prompt patterns for focused AI code review that ask for high-risk bugs, line evidence, reproduction steps, missing tests, and confidence notes.

A useful AI code review prompt asks for specific high-risk findings with file and line evidence. It does not ask for every possible improvement. Broad prompts create broad noise, and noisy review tools quickly lose reviewer trust.

The goal is to turn AI review into a defect-finding assistant. Ask for bugs, regressions, security issues, missing tests, and behavior changes introduced by the diff. Require the model to explain why each finding matters and how a reviewer can reproduce or test it.

Use this guide with the AI Code Review Checklist and AI Code Review Workflow when building a repeatable review process.

Treat the prompt as a review instrument. If it produces noise on clean changes, it needs the same kind of debugging as a flaky test.

Start with a narrow review role

The review prompt should name the role and the boundary:

Review this diff for high-risk defects introduced by the changed code. Focus on correctness, security, data handling, missing tests, and regressions. Do not comment on style unless it causes a defect.

This tells the model what not to do. The exclusion matters. Without it, the review may fill with naming preferences, formatting suggestions, and generic advice.

Add project context after the role: language, framework, test command, security assumptions, and relevant repo instructions. Keep it short enough that the model can still focus on changed code.

Require evidence for every finding

A review finding should have a minimum shape:

• File and line reference.
• Defect category.
• Why the behavior is wrong.
• How to reproduce or test it.
• Confidence level.
• Suggested fix direction, not a full rewrite unless asked.

Use a prompt like:

For each finding, include: file:line, severity, defect category, evidence from the diff, a reproduction or test idea, and why this must be fixed before merge.

If the model cannot provide line evidence, the finding should be treated as a hypothesis. Hypotheses can be useful, but they should not block a merge without reproduction.

Ask for missing tests separately

Missing tests are a review category, not an afterthought. Ask the model to identify which changed behavior lacks proof.

After listing defects, list missing tests that would prove the requested behavior or catch the highest-risk regression. Do not invent broad test rewrites.

This keeps test advice focused. A model may otherwise propose a large test suite that nobody will add. Good test suggestions are tied to the changed behavior and likely failure paths.

For test selection, use AI Code Verification Tests and AI-Generated Code Testing.

Add a false-positive control

Ask the model to separate confirmed findings from uncertainties:

If a concern depends on missing context, mark it as "needs reproduction" instead of presenting it as a confirmed defect.

This improves reviewer trust. It is acceptable for the model to say that a finding needs reproduction. It is not acceptable for it to present every guess as a defect.

You can also ask for “top five findings max” or “only severity high and medium.” Caps reduce noise and force prioritization.

Test the prompt against fixtures

Do not judge a review prompt from one pull request. Freeze a few fixtures: one PR with a clear bug, one with a subtle edge case, one security-sensitive change, and one clean PR. The clean PR is important because it measures false positives.

Record:

• Bugs found.
• Bugs missed.
• False positives.
• Whether findings had line evidence.
• Reviewer cleanup time.
• Whether suggested tests were useful.

The Prompt Testing Template can turn those cases into a repeatable review prompt suite. The public Best AI for Code Review benchmark rubric uses the same evidence-first principle before any recommendation can be published.

Verification checklist

Before using an AI code review prompt in a team workflow, confirm:

• The review role is narrow.
• Style comments are excluded unless requested.
• Findings require file and line evidence.
• Missing tests are requested separately.
• Uncertain findings are labeled as needing reproduction.
• The prompt was tested on at least one clean PR.
• Reviewer decisions are logged.

FAQ

What makes an AI code review prompt useful?

A useful AI code review prompt asks for specific high-risk findings with file and line evidence.

What should code review prompts avoid?

Code review prompts should avoid generic style feedback unless style is the explicit review goal.