How to Review Code Written by an AI
A checklist-driven approach to reviewing AI-generated code, focused on the failure modes that differ from typical human-written pull requests.
Read the diff like you don't trust it yet
AI-generated code tends to look more finished than it is, with consistent formatting, plausible variable names, and comments that sound authoritative even when they describe the wrong thing. That polish is exactly why it deserves closer reading than a typo-ridden pull request from a rushed human, not less.
Read every line the way you would review a stranger's contribution to a codebase you are responsible for: assume nothing about intent, and confirm that what the code actually does matches what the task asked for, not just what the description claims it does.
The failure patterns worth specifically checking for
AI-written code is prone to a handful of recurring issues: silently swallowed exceptions, edge cases dropped because the training data underrepresented them, and confident-looking error handling that never triggers because the condition is wrong. It will also happily invent a plausible-looking API call or config option that does not exist in your dependencies.
Check that every import and function call actually resolves, that tests were added for the behavior being changed rather than just for the happy path, and that nothing was deleted or disabled to make a failing test pass.
- Verify every referenced API, method, and config key actually exists
- Check exception handling for silently swallowed errors
- Confirm tests cover the real change, not just the happy path
- Look for deleted or weakened tests used to force a green build
- Check for scope creep: files touched that the task didn't require
Adjust your review effort, not just your checklist
Treat the size and reversibility of the change as your guide to how much scrutiny it needs. A one-file formatting change deserves a glance; a change touching authentication, payments, or data migrations deserves the same rigor as if a new hire had written it on their first week.
Over time you will learn which categories of task a given agent handles reliably and which it consistently gets wrong. Use that history to calibrate review depth instead of treating every AI-generated diff identically.
Key takeaways
- Apply one concrete change from this post before collecting more reading.
- Prefer browser-side tools when the work involves secrets, tokens, or PII.
- Document the why next to the how so the next reviewer inherits context.
FAQ
- Who is this guide on ai for?
- Working developers who need a practical take on how to review code written by an ai — not a marketing overview. Skim the sections, apply one tip, then come back when you hit an edge case.
- Do I need an account to use the related tools?
- No. code.live tools run in your browser with no signup. Nothing you paste is uploaded to a server for the client-side utilities linked from this post.
- How often is this article updated?
- This post was published October 18, 2026. Fundamentals stay stable; check linked tool pages and official docs when version-specific behavior matters.