How to Review AI-Generated Code Before You Ship It
The five specific places bugs hide in AI-generated code, a concrete worked example, and a five-minute review process that actually fits a real schedule.
AI-generated code fails in five specific, recurring places, and knowing those five places turns code review from reading every line back into something that fits into a real schedule. The code that looks the most finished, clean formatting, sensible variable names, confident comments, is exactly the code most likely to hide a fabricated function call or a skipped edge case, because those are cosmetic signals that have nothing to do with correctness.
The five places bugs actually hide
Where | What to check | Why it hides well |
|---|---|---|
Fabricated APIs | Does every function, library method, and parameter actually exist in the version you are using | Reads exactly like real code, often with plausible parameter names |
Missing edge cases | Empty input, network failure, zero results, the second time a form is submitted | The happy path always looks complete |
Authorization gaps | Can this endpoint or query be reached by a user who should not have access | The feature works perfectly for the one legitimate user tested |
Swallowed errors | Does a catch block hide a real failure instead of handling it | The app appears to work; it just fails silently instead of loudly |
Near-miss copy logic | Reused code adapted for a new case: check every changed variable, not just the new lines | Looks like correct, familiar code because most of it is |
Fabricated APIs: the fastest check
Before anything else, run it. A function or method that does not exist fails immediately and loudly, which makes this the cheapest bug on the list to catch and one of the most common in AI-generated code, since a model can describe a plausible-sounding API with total confidence regardless of whether it is real. This is a direct instance of AI hallucination: the model is not lying, it is producing the statistically likely next token, which for a rarely used library can easily be a method that sounds right and does not exist.
Missing edge cases: the one that reaches production
AI-generated code overwhelmingly handles the case it was asked about and underhandles the cases it was not. Asked to "add a function that calculates the discount," it is likely to handle a normal price and a normal discount percentage correctly and mishandle a zero price, a discount over 100 percent, or a missing value, because none of those were in the request. The fix is a specific habit: for every new function, ask what happens with empty input, a huge number, a negative number, and a second identical request, before considering it done.
Authorization gaps: the quiet one
This is the same failure mode covered in adding user accounts to an AI-built app: a feature that works correctly for the one account it was tested with can still let any other account reach data it should not. AI-generated code frequently gets the feature logic right and the access control as an afterthought, because the prompt asked for the feature, not for the permission boundary around it. Check every new endpoint or query specifically for who else could call it, not just whether the intended user's flow works.
Swallowed errors: the bug that looks like success
A try/catch block that catches an error and does nothing, or logs it quietly and continues, turns a loud, obvious failure into a silent one. AI-generated code reaches for broad error handling readily, because it makes the code look robust, but a catch block with no real handling inside it is often worse than no catch block at all: the failure still happens, it just stops being visible.
Near-miss copy logic: the subtlest one
When an AI tool adapts existing code for a new but similar case, for example duplicating a filter function for a second data type, the bug usually lives in exactly one changed variable that was not updated everywhere it needed to be. This is the hardest of the five to catch by skimming, because 95 percent of the code is correct and familiar-looking. It rewards a specific, narrow check: for any copied-and-adapted block, trace every variable that changed and confirm it changed everywhere its old value was used.
Let tools catch what tools are good at
A linter and a type checker, run automatically before any human review starts, catch a meaningful share of the near-miss and syntax-level issues for free: unused variables, obvious type mismatches, unreachable code. Neither catches fabricated APIs that happen to match the expected type signature, missing edge cases, or authorization gaps, because none of those are syntax problems, they are correctness and access-control problems that require understanding what the code is supposed to do, not just whether it parses. Use automated checks as a fast first pass, not as evidence the code is safe to ship.
A concrete example
An AI coding tool is asked to add a "resend confirmation email" button to an existing signup flow. It correctly reuses the existing send-email function. The bug: it also reuses the existing rate limit check, which was written for the original signup email and allows only one send per account ever, not one resend per day as the new feature actually needs. The feature demos perfectly the first time any tester clicks it, because a fresh test account has never sent that email before, and only fails for a real user on their second resend attempt, which is exactly the case demoing does not surface and production does.
A lightweight review process
Run it before reading it. Fabricated APIs and obvious runtime errors surface immediately and cost nothing to catch this way.
Read only the new or changed lines closely, not the whole file. Trust the untouched code that was already working.
For every new function, name the four edge cases (empty, huge, negative, repeated) out loud and check each one.
For every new data access, ask who else could reach it, not just whether the intended path works.
For every catch block, confirm it does something real: log with enough detail to debug, surface an error to the user, or retry, not silently continue.
This is a fifteen-minute habit for a small change, not a formal review process, and it catches the specific failure modes above rather than trying to read every line with equal attention, which does not scale and, per why AI writes code that does not work, is not actually where most AI coding failures come from anyway.
Ownership is a separate question from quality. See who owns AI-generated code for how copyright, training-data rights, and vendor terms actually split.
Frequently asked questions
Do I need to read every line of AI-generated code?
No. Reading every line with equal attention is how review time balloons and quality does not improve proportionally. Targeted checks on the five failure modes above catch more real bugs per minute spent than a uniform line-by-line read.
How is this different from normal code review?
The categories are the same ones good code review has always looked for. What changes with AI-generated code is the base rate: fabricated APIs and unrequested edge cases are more common than in code a careful developer wrote by hand, because the model was never asked about the case it silently skipped.
Can automated testing replace this review?
Tests catch what they were written to check for, and AI-generated tests share the same blind spot as AI-generated code: they tend to test the case that was asked about. Automated tests are a strong complement to this review, not a replacement for checking the specific failure modes above.
What if I am not a developer and cannot read the code?
Focus on behavior instead of code: test with unusual input (blank fields, huge numbers, doing the action twice), test with a second account if the app has accounts, and watch for anything that fails silently rather than showing an error. That approach catches most of the same issues without reading a line of code.
Should I trust code more from a newer or more capable AI model?
Trust it to be more fluent, not to be free of these five failure modes. Newer models produce cleaner, more confident-looking code, which if anything makes fabricated APIs and skipped edge cases harder to spot on a skim, not easier. The review habit does not change with model quality.
How much time should code review actually take?
For a small, well-scoped change, the five-step process above genuinely takes minutes, not hours. The time cost scales with how much new logic was added and how much of it touches data access or money, not with the raw line count.
Review catches some issues before they ship. For the ones that slip through, see how to debug AI-generated code step by step
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About the author
Senior Editor, AI & Product
Cecilia leads the Swarmz editorial desk. She has spent a decade turning complex AI and product topics into writing people actually finish, and she owns the blog's quality bar.
