We’ve already written about why we don’t “vibe code” — why AI-generated output goes through the same review discipline as anything else before it ships. The natural follow-up question is: what does that review actually consist of? Not as a vague principle, but as an actual checklist.
What we actually check
Does it match how the system is really built, not just how it looks like it should work? AI-generated code is frequently plausible — syntactically correct, reasonably structured — without actually reflecting the specific architecture, naming conventions, or data model of the project it’s being dropped into. The first check is always: does this fit what’s actually here, or does it just look like code that would fit somewhere?
What happens on the edge cases nobody asked about? A generated solution usually handles the case it was prompted for cleanly. Empty input, malformed input, a network timeout mid-request, a user submitting the same form twice — these are exactly the cases that don’t show up in a quick prompt-and-generate loop, and exactly where real bugs hide.
Does it introduce a security assumption nobody signed off on? Generated code sometimes trusts input that shouldn’t be trusted, or skips a validation step that seems redundant in isolation but isn’t. This gets checked explicitly, not assumed away.
Would the next person maintaining this understand why it’s written this way? Code that works but that nobody — including the person who generated it — can explain six months later is a liability with a delay timer on it.
“The model can produce something that runs. It can’t tell you whether it’s the right thing to have built, or whether the person shipping it actually understands what it does. That judgment is still entirely a human job.”
The takeaway
None of this is exotic — it’s the same review discipline that should apply to any code, AI-assisted or not. The difference is that AI-generated code can look more finished than it is, which makes skipping the review more tempting, not less necessary.
