AI Code Review Interviews: How to Critique Generate...

AI can produce code faster than teams can safely accept it. That is why code review interviews are becoming more important.

The question is no longer only "Can you write code?" It is also "Can you judge code?"

What the Interview Really Tests

A code review interview is not a chance to prove that you are smarter than the author. It tests whether you can protect the system while preserving collaboration.

Interviewers usually listen for five things:

can you find correctness issues
can you identify missing tests
can you explain operational or security risk
can you separate blocking problems from preferences
can you give feedback the team could actually use

In the AI era, there is one more signal: can you critique plausible generated code?

AI-generated code often looks clean on the surface. The risk is hidden in assumptions, missing context, outdated APIs, weak error handling, and untested edge cases.

Review in Layers

Do not start with naming style. Start with risk.

Intent

What is this change trying to do? Does the implementation match the requirement?

Correctness

What inputs break it? What state transitions are unsafe? What happens when the dependency fails?

Tests

Are important paths covered? Are edge cases covered? Would the test fail before the fix?

Maintainability

Is the code easy to understand? Does it follow local patterns? Does it introduce unnecessary abstraction?

Security and operations

Could it leak data, double-charge usage, create race conditions, or hide failures?

Only after those layers should you mention style.

A strong reviewer says: "The main approach makes sense, but I would block merge until we verify the empty input path and the retry behavior. If retries call this function twice, we may double-count the action."

The strong version names the risk, explains the condition, and suggests verification.

Another useful phrase:

"I would treat this as a blocking comment because it can affect user data. The naming issue is non-blocking and can be handled separately."

That prioritization makes you sound senior.

How to Review AI-Generated Code

When the interviewer says the code was generated by an AI assistant, do not turn the answer into a speech about AI. Review the code the same way you would review any code, but pay special attention to hidden assumptions.

Ask:

did the code infer requirements that were never stated
did it choose a library or API that exists in this project
does it handle errors the same way the codebase does
does it add complexity the task does not need
are the tests proving behavior or only matching the generated implementation

These questions show that you understand the real risk of AI coding: confident misalignment.

Example Answer

"I would start by confirming the intended behavior. The implementation looks reasonable for the happy path, but I see three risks before merge.

First, the empty input case returns success without explaining whether that is expected. I would add a test to define the behavior.

Second, the retry path appears to call the write operation again. If this action is not idempotent, we could create duplicate records.

Third, the error handling logs the raw payload. If the payload can include user content, that could expose sensitive data.

I would block on the retry and logging issues because they affect data integrity and privacy. I would leave the naming comment as non-blocking."

Where Interview AiBox Helps

Code review interviews are hard because you must think and communicate at the same time. Interview AiBox can help you rehearse the speaking pattern: observe, prioritize, explain, and propose verification.

Instead of saying "this might break," practice saying "this assumption fails when input is empty, and I would verify it with a regression test before merging."

That is the difference between noticing a problem and reviewing like a teammate.