5 Ways to Use AI for Code Review Without Breaking Things

Code review is one of the best uses of AI in software development. An AI assistant can read your diff in seconds, point out a missed null check or an off-by-one error, and suggest tests you forgot. It does not replace a human reviewer, but it can make sure the obvious problems are fixed before anyone else looks.

Here are five practical ways to use AI for code review, with prompts you can copy.

1. Self-review before opening a pull request

The highest-value habit: review your own change with AI before you ask a colleague.

Review this diff as a strict senior reviewer. The change is supposed to:
[one-line description]. List, in order of severity:
- bugs and logic errors
- unhandled edge cases (nulls, empty lists, large inputs, time zones)
- anything that changes behaviour beyond the stated goal
For each issue give the line and a suggested fix. Do not rewrite the
whole file.

[paste git diff]

Giving the intended goal matters. Without it, the AI cannot tell a bug from a deliberate change.

2. Hunting edge cases

AI is good at imagining inputs you did not think about:

For this method, list 10 inputs that could make it fail or behave
unexpectedly, including boundary values and invalid data. For each,
say what the current code would do. [paste method]

Turn the interesting ones into unit tests. If the AI says "this would throw", write the test and see whether it really does.

3. Reviewing tests, not just code

Tests written in a hurry, by people or by AI, often check too little:

Review these unit tests. Which important behaviours of the method are
not tested? Which tests would still pass if the method returned a wrong
value? Suggest the 3 most valuable missing tests.
[paste method and tests]

A test suite that cannot fail is worse than none, because it gives false confidence.

4. A security pass

Ask for a focused security review on code that handles input, authentication, files or database queries:

Review this code for security issues: injection, missing authorisation
checks (can a user access another user's data?), unsafe deserialisation,
secrets in code, and unsafe file paths. List each issue with the line
and a fix. [paste code]

Follow up with your normal tools as well: static analysis, dependency vulnerability checks and your company's security scanners. AI review is an extra layer, not a replacement.

5. Readability and naming

Code is read far more often than it is written:

Suggest improvements to naming and structure that would make this code
easier for a new team member to understand. Keep behaviour identical.
Only suggest changes that are clearly worth it. [paste code]

"Only suggest changes that are clearly worth it" stops a long list of personal-taste nitpicks.

Where AI review falls short

Be clear about the limits:

• It does not know your system. It may not see the caller that depends on the old behaviour, the database constraint or the business rule documented elsewhere.
• It can be confidently wrong. Some flagged "bugs" are not bugs, and some real bugs are missed. Verify before acting.
• It is not accountable. A human still approves the merge and owns the result.

Treat AI comments the way you would treat comments from a smart new colleague: often useful, sometimes wrong, always worth checking.

Rules for using AI review safely

• Follow company policy. Only paste proprietary code into tools your company approves.
• Never paste secrets. Remove connection strings, API keys and tokens from diffs and config before sharing.
• Keep diffs small. AI reviews of 50 lines are far more accurate than reviews of 2,000 lines, just like human reviews.
• Commit before applying AI fixes, so you can see and undo exactly what changed.

A simple workflow

1. Finish your change and run the tests.
2. Run the self-review prompt on your diff and fix real issues.
3. Run the edge-case and test-review prompts on the core logic.
4. For sensitive code, run the security prompt.
5. Open the pull request with a short description of what changed and why.

Your reviewers will spend their time on design and business logic instead of missed null checks.

For a complete, practical guide covering debugging, tests, refactoring, EF Core queries and security, see our free course AI for Developers.