Using AI for code review without getting false confidence

AI can review code faster than a human. It can also miss the things that matter most and give you a false sense of coverage.

Here's how to use it as a useful first pass without treating it as a substitute for real review.

What AI code review catches well

Surface-level issues:

• Obvious null reference bugs
• Missing error handling for common cases
• Hardcoded values that should be config
• Unused variables and imports
• Obvious security issues (SQL injection patterns, credentials in code)
• Style and naming inconsistencies

Pattern matching:

• "This is similar to X but doesn't handle the Y edge case that X handles"
• "This duplicates logic that exists in /lib/utils.ts"
• "This approach doesn't follow the convention used in the rest of the codebase"

What AI code review misses

Business logic correctness. The model doesn't know what the code is supposed to do in the context of your product. It can check if the code is internally consistent but not if it's right for your use case.

System-level interactions. How this change interacts with the queue worker running in production, the cron job that runs at midnight, the edge case that only appears under specific load conditions — these require human context.

Intentional non-obvious decisions. "Why is this structured this way" often has a history. The model will flag it as suspicious. The reviewer who knows the history knows it's intentional.

The useful prompt

Review this code for:
1. Logic errors that will cause incorrect behavior
2. Security vulnerabilities
3. Missing error handling
4. Performance issues (N+1, unbounded operations)
5. Anything that doesn't match the conventions in [relevant file]

For each finding: severity (blocker/warning/note), specific location, proposed fix.
Do not comment on formatting or style that doesn't affect correctness.
Do not flag intentional patterns — if something looks unusual but is consistent with the codebase, skip it.

The right position in your review process

AI review first, then human review. Not instead of.

Use AI to catch the mechanical issues so the human reviewer can focus on logic, architecture, and business correctness. "The AI already checked for obvious bugs" is a valid reason to spend review time on higher-value concerns.

What to do with AI review output

Don't auto-apply suggestions. Read each one. Ask: is this actually a problem or is it a conservative suggestion? For security findings especially — confirm the vulnerability is real before treating it as a blocker.

AI reviewers are thorough but cautious. They will flag things that aren't issues. The value is in the signal-to-noise ratio, which is high enough to be worth using.

More on reviewing AI-generated code: builtbyzac.com/power-moves.html.