August 21, 2026·7 min read
Validating AI-generated code means checking that it does what you asked, not just that it looks like it does. That distinction sounds small. It is the whole problem. Generated code reads as finished: consistent style, sensible names, a docstring that explains itself. None of that tells you whether it is correct, and the two have become disconnected in a way that human-written code rarely manages.
This post is a practical answer to "how do I actually validate this," not a warning about AI code in the abstract. It gives you a way to decide how much scrutiny a given piece of code needs, a floor of checks that apply no matter what, and a worked example so the checklist isn't just theory.
Code review assumes a colleague wrote the code under normal human constraints: they got tired near the end, they understood some parts better than others, they probably tested the part they were unsure about and skimmed the part that felt obvious. Reviewers learned, correctly, to focus attention where a human is most likely to have made a mistake.
Generated code breaks that assumption. It does not get tired. It is not less confident about the part it is worse at. A model can produce a call to a method that does not exist with exactly the same fluency and tidiness as a call to one that does. There is no tell in the prose. The only way to find the difference is to check, not to read more carefully.
That is what "validate" means here: a specific, checkable claim gets tested against reality, instead of judged by how plausible it reads. Three checks do almost all of the work.
Everything below is a way of applying those three checks with the right amount of effort for what the code is actually going to do.
The mistake we see most often is uniform scrutiny: either everything gets a five-second glance because it looks fine, or everything gets the same heavy review regardless of what it touches. Neither matches how risk is actually distributed in a codebase.
A useful way to split it is by what happens if the code is wrong and nobody notices for a month.
| Tier | What it looks like | Minimum validation |
|---|---|---|
| Low stakes | Throwaway scripts, one-off data pulls, internal tooling nobody depends on | Run it once against real input. If the output looks right and nothing depends on it later, that is enough. |
| Application logic | Business rules, request handlers, anything a user's action flows through | Run it against the real case, not the model's own test. Name the edge cases out loud (empty, null, duplicate, huge) and try the one most likely to occur. Check that referenced functions and fields exist in your actual dependency versions. |
| High stakes | Authentication, payments, anything touching secrets, data deletion, infrastructure changes | All of the above, plus: read every line yourself and be able to explain it to someone else. Check the failure mode specifically: what happens on a bad credential, a timeout, a partial write. Assume the most common online pattern is the insecure one, because that is often what the model reproduces. |
The tier is a property of the code's blast radius, not of how impressive the generated output looks or how long the prompt was. A three-line function that decides whether to refund a customer is high stakes even if a model wrote it in one shot and it looks trivial.
Stanford researchers who ran a controlled study on this found the effect goes the wrong way if you rely on intuition instead of a rule like this: participants who used an AI assistant to write code wrote measurably less secure code than a control group, while rating their own code as more secure than it was (Perry et al., "Do Users Write More Insecure Code with an AI Assistant?"). Confidence and correctness moved in opposite directions. That is exactly the failure mode a fixed, risk-based floor is meant to catch, because it does not depend on how confident the code feels.
Here is what this looks like on an actual piece of generated code, rather than in the abstract.
The task: write a function that calls a flaky internal API, retries on failure with exponential backoff, and gives up after five attempts. A model produces something clean: a loop, a growing sleep interval, a try/except around the call, sensible variable names. It looks exactly like what a senior engineer would have written.
Running the three checks:
Does it exist? The retry loop calls a requests.Session method with a timeout keyword. That exists and is spelled correctly in the installed version. No hallucinated call here, so this check passes in under a minute.
Does it do what was asked? Run it against the actual flaky endpoint, not a mock. On the first real run, it works twice out of three tries, which is what you would expect from a flaky dependency. Looks fine so far.
Does it fail safely? This is where it falls apart. The except block catches the request exception, logs a line, and after the fifth failure, returns None instead of raising. Nothing downstream expects None. The function was asked to "give up after five attempts," and it technically does, it just gives up silently instead of telling the caller it gave up.
Nobody would catch this by reading the code twice. It reads as reasonable: a bounded retry loop with logging is a normal pattern. You catch it by asking the third question directly, out loud, instead of trusting that clean code implies correct behavior: what happens when it actually fails five times? That one question, asked on purpose, is the entire difference between shipping this and not.
Regardless of tier, these three take about five minutes combined and catch the majority of what goes wrong. Skip them and you are reviewing style, which generated code is already good at.
If you have time for one more, add: could you explain this to someone else without opening the model's chat history again? If the answer is no, you have not finished, you have accepted.
Everything above is a personal habit, useful whether or not anyone is watching. It also happens to be one of the clearest ways to tell candidates apart, because "I use AI to write code" has stopped meaning anything on its own. Two people can say that sentence and mean entirely different levels of care.
We put candidates in a real environment with a real task, let them use whatever tools they want including a model, and record the session. The gap between someone who runs the five-minute floor by habit and someone who ships whatever compiles shows up in the first few minutes, not at the end of the hour. We break down what that split actually looks like, task by task, in four AI skills and how to test each, and go deeper on the reviewer's side of this same problem, deciding whether to accept someone else's generated pull request, in how to review AI-generated code. If you want the broader framework this slots into, our AI literacy rubric scores this exact behavior, checking the answer, as one of four things worth grading on its own.
Validating AI-generated code is not a heavier version of code review. It is a smaller, sharper set of questions aimed at the specific way generated code fails: confidently, in the parts that look easiest. Match the depth of the check to what the code can actually break, run the five-minute floor every time regardless of tier, and ask what happens when it fails instead of trusting that clean code implies correct code.
The next piece of generated code you're about to accept, pick one of the three questions above and actually answer it before you merge. That single habit catches more than any amount of careful reading.
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