What a Rigorous Review of AI-Generated Code Actually Requires
AI coding tools produce code that looks correct far more often than it is correct. Our review standard is built around the specific ways that code fails, not a generic style checklist.
Reviewing AI Output Is Not the Same Skill as Reviewing a Colleague’s Code
Human code review leans on cues that AI-generated code rarely gives you. Careless human work tends to look careless — inconsistent style, an obvious shortcut, a comment admitting the hack. AI output is usually well formatted, consistently named and confidently structured whether or not it is correct. The visual signals a reviewer normally relies on to know where to look closely are mostly absent, which means the review has to be more deliberate, not less.
"AI code review standards" is our name for the specific discipline of reviewing that kind of output: knowing the failure patterns AI-generated code actually exhibits, checking for them directly instead of skimming for tidiness, and treating a passing test suite as a starting point rather than a verdict. It is the second half of what we vet for, and it is what we expect from every developer on the platform once they are actually working, not just during assessment.
Specific Failure Patterns, Not a Vague Warning
These are the patterns a rigorous reviewer checks for by name, because "be careful with AI code" is not an instruction anyone can actually follow.
Rigorous Review vs. a Rubber Stamp
Both can look identical from the pull request title. The difference is entirely in what the reviewer actually did.
Checks that it runs and looks tidy
- Skims for formatting and naming, because those are what stand out visually.
- Treats a green test run as proof the change is correct.
- Approves because the explanation in the pull request description sounds confident.
- Never asks what input would break this.
Checks whether it is actually correct
- Can explain why every line is correct without saying "the assistant wrote it that way."
- Checks the API calls used actually exist on the version of the library in the project.
- Tries the input nobody thought to write a test for.
- Notices when a fix quietly removed a check instead of addressing the actual bug.
The Same Standard, Two Places
This is not a marketing checklist that stops mattering once someone is hired. It is the standard candidates are tested against, and the standard our developers work to afterwards.
In Vetting Candidates
This standard is the basis of the AI-assisted work review on our vetting process. Candidates are handed real work samples and asked to review them; we watch whether they catch the failure patterns above or skim past them the way a rubber stamp would.
In How Our Developers Work
Once hired, a developer is expected to hold their own AI-assisted output to this bar before it reaches your pull requests. It is also, directly, what we teach in team training engagements where a client’s own developers are the ones learning to apply it.
Questions About Review Standards
What separates a real review from a "rubber stamp" review?
A rubber stamp checks that the code runs and looks tidy. A real review asks what happens on the input nobody tried, what happens when the third-party call fails halfway, and whether the reviewer could explain each line to someone else without saying "the assistant wrote it that way." If a reviewer cannot explain why a piece of AI-generated code is correct, they have not actually reviewed it.
Why is AI-generated code harder to review than code a person wrote badly?
Because it rarely looks bad. A human writing careless code often leaves signs — inconsistent style, obvious shortcuts, a comment admitting the hack. AI-generated code tends to be well formatted, consistently named and confidently structured regardless of whether it is actually correct, so the usual visual cues for "this needs a closer look" are mostly absent. The review has to be more deliberate because it cannot rely on sloppiness as a warning sign.
Does a passing test suite mean AI-generated code is safe to ship?
No, and treating it as sufficient is one of the more common mistakes we see. AI tools are good at writing code that satisfies the test they were shown, including a test they wrote themselves for the same change. That is a much narrower claim than correct on the inputs your users will actually send. A passing suite is a starting point for review, not a substitute for it.
How is this standard applied when vetting candidates?
It is the basis of the AI-assisted work review described on our vetting process page. Candidates are given real work samples and asked to review them, and we watch whether they catch the specific failure patterns described here — a plausible security hole, a hallucinated API call, an edge case quietly dropped — rather than skimming for style issues.
How is this standard applied once a developer is actually hired?
The same standard, not a different one used only for show during vetting. Developers on project or team-augmentation engagements are expected to review their own AI-assisted output to this bar before it reaches your pull requests, and it is what we teach directly in team training engagements where a client’s own developers are the ones learning to apply it.
Does this mean AI coding tools should not be trusted?
No — it means they should be reviewed the way any fast, confident, occasionally wrong collaborator should be reviewed. The tools are genuinely useful for speed on scaffolding, boilerplate and first drafts. The standard here exists because speed without review is how a subtly wrong security check or a hallucinated method call reaches production looking exactly like correct code.
Want Developers Who Already Review to This Bar?
Every profile we send has cleared the AI-assisted work review this standard is built from.