Developer & Tech
How to Review AI-Generated Code Without Slowing Down
By Jim Vernon, Editor, AI Intelligence International · Published 25 March 2026 · Reviewed against our editorial standards · About the author
Reviewing generated code with the same instincts you use on human code wastes time in the wrong places. Human reviewers look for carelessness, and generated code is never careless — it is confidently, uniformly wrong in specific structural ways instead.
This article maps where those failures cluster and gives a review order that catches most of them in the first two minutes.
Key takeaways
- It is right locally and wrong globally: The most common failure is code that does exactly what was asked in isolation while ignoring a constraint that exists elsewhere in the system — an established error convention, a transaction boundary, a caching layer, a permission check applied everywhere else.
- Error handling is where it is thinnest: Happy paths are heavily represented in training data and failure paths are not.
- Security defaults: Generated code frequently reflects the median example rather than the secure one: string-interpolated queries, permissive CORS, unvalidated input, secrets read at module scope, authorisation checked in the UI rather than the handler.
- Dependencies and invented APIs: Models sometimes reference packages that do not exist, or methods a real package does not have.
It is right locally and wrong globally
The most common failure is code that does exactly what was asked in isolation while ignoring a constraint that exists elsewhere in the system — an established error convention, a transaction boundary, a caching layer, a permission check applied everywhere else.
Read generated code against your codebase's conventions first, before reading it for correctness. The logic is usually fine; the integration usually is not.
Grep for how the same operation is done elsewhere. If the generated version does it differently with no reason, that difference is the review comment.
Error handling is where it is thinnest
Happy paths are heavily represented in training data and failure paths are not. Generated code tends to catch broadly, swallow silently, or assume the operation succeeded.
Check every external call for what happens on timeout, on partial success and on malformed response. These three cover most production incidents traceable to generated code.
Watch for catch blocks that log and continue. They convert a loud failure into a silent data problem, which is far more expensive.
Security defaults
Generated code frequently reflects the median example rather than the secure one: string-interpolated queries, permissive CORS, unvalidated input, secrets read at module scope, authorisation checked in the UI rather than the handler.
Treat any generated code touching authentication, authorisation, file paths or user input as requiring a full manual read regardless of how routine it looks.
Ask specifically whether the check is enforced server-side. A client-side guard that looks correct is one of the most common generated vulnerabilities.
Dependencies and invented APIs
Models sometimes reference packages that do not exist, or methods a real package does not have. Both fail fast, which is good, but they also sometimes reference a package that does exist and does something else entirely.
Verify every new import. Check that the package is real, maintained, appropriately licensed, and not a typosquat of the one intended.
Also check version assumptions. Code written against an older major version compiles and then behaves differently at runtime, which is the worst failure category.
Tests generated alongside the code
Tests written by the same process that wrote the code test what the code does, not what it should do. They pass on the bug.
Read generated tests for whether the assertions encode the requirement or merely mirror the implementation. Mirrored assertions are worse than no tests, because they create confidence.
Write at least one test yourself from the requirement, without looking at the implementation. That one test does most of the work.
A review order that is fast
One: imports and dependencies. Two: anything security-relevant. Three: error paths on external calls. Four: convention alignment with the surrounding codebase. Five: the actual logic.
Logic last is deliberate. It is the part generated code most often gets right and the part reviewers instinctively start with.
For small diffs this order takes about two minutes and catches the overwhelming majority of real problems.
Worked example: a generated endpoint
Task: add an endpoint returning a user's orders. Generated code was forty lines, compiled, and worked in manual testing.
Import check: clean. Security check: the handler read the user id from a query parameter rather than from the session, so any authenticated user could read any other user's orders. That is the whole review, found in step two.
Error path check: the database call had no handling for a failed connection, and the outer catch returned a 200 with an empty array — a silent failure that would have looked like a user with no orders.
Convention check: every other handler in the codebase used a shared result wrapper; this one returned a raw object, which would have broken the client's error handling.
Logic, read last, was entirely correct. Three real defects, none of them in the part the code was actually asked to do.
Review in a different order
Human-written code is usually reviewed line by line, because the author's intent shows in the structure. Generated code is better reviewed from the outside in: does the interface make sense, does it handle the boundary cases, then read the body.
Check first for the failure modes specific to generation — an invented API, a silently swallowed error, an unused parameter, a test that asserts nothing, or an off-by-one in a boundary the prompt never mentioned.
Run the tests before reading them. Generated tests that pass without exercising the change are common enough that verifying they fail against the old code is worth the thirty seconds.
Frequently asked questions
Should generated code be reviewed more strictly than human code?
Not more strictly, differently. The failure distribution is distinct — less carelessness, more convention and integration failure — so the review attention should be redistributed rather than increased.
Can AI review AI-generated code?
It catches some classes of issue, particularly style and obvious error-handling gaps, but it shares the blind spots of the code it is reviewing. Use it as a first pass, never as the only pass.
What is the single most dangerous generated pattern?
Authorisation checked in the wrong place. It works in every test, passes review by anyone reading for logic, and exposes data in production.
Does this slow teams down?
The structured order is faster than an unstructured read, because it front-loads the categories where defects actually cluster. Most reviewers find it saves time rather than costing it.
Should pull requests declare that code was AI-assisted?
A short note helps reviewers calibrate attention, but the author remains fully accountable either way.