By 2026, most engineers in tech jobs are reading AI-generated code daily. Either they wrote a prompt and the model produced a draft, or a teammate did. The skill of effectively reviewing AI-generated code has become as essential as writing code itself — and the interview questions follow this shift.
What is different about AI code
AI-generated code has characteristic failure modes that human-written code rarely has:
- Plausible-but-wrong: code that compiles, looks reasonable, but does not match the spec
- Hallucinated APIs: calls to functions or libraries that do not exist
- Missing edge cases: the model nailed the happy path; off-by-one, null handling, and concurrency are frequently missed
- Verbose where unnecessary: models default to defensive code that is not always warranted
- Stale patterns: for newer frameworks or recent API changes, models often produce out-of-date code
The review checklist
- Does it actually solve the problem? Run it against test cases, including edge cases.
- Does it use real APIs? Verify imports and method signatures.
- Are the assumptions correct? Models pattern-match; they may have assumed a similar problem.
- Are the edge cases handled? Empty input, null, boundary values, concurrent modification.
- Is the structure idiomatic to your codebase? Models do not know your conventions.
- Are the tests meaningful? AI-generated tests sometimes test the implementation, not the behavior.
- Is there security risk? SQL injection, XSS, deserialization issues are common in AI output.
What to verify, what to trust
Trust:
- Boilerplate and structural patterns
- Standard library usage in well-established languages
- Test scaffolding
Verify:
- Anything that talks to a database, file system, or network
- Anything involving concurrency or async
- Anything in a security boundary
- Anything using new or niche libraries
The “plausible-but-wrong” trap
The most dangerous AI output looks correct on first read. Examples:
- Off-by-one in a loop boundary
- Wrong default value when an input is missing
- Subtly wrong regex (e.g., greedy when lazy is needed)
- Wrong error class thrown (will not match catch block)
Counter: read the diff slowly. Mentally execute against simple inputs. Don’t skim.
Pair-programming with AI
Best practices that have emerged:
- Generate a draft, then read every line
- Ask the model to explain non-obvious choices
- Have a strong test case before generating, so you can verify quickly
- Use models for refactors and translations more than for novel design
Interview questions on this topic
Increasingly common:
- “Walk through how you reviewed an AI-generated PR last week”
- “Show me a time AI-generated code looked correct but was wrong”
- “How do you decide when to use AI vs write it yourself?”
Strong answers cite specific examples with technical detail.
Skills senior engineers need
- Critical reading — slow down for AI output
- Test-first thinking — your tests are how you trust the code
- Domain expertise — you must know the right answer to recognize wrong
- Communication — explain to less experienced teammates why a “looks fine” PR is wrong
Frequently Asked Questions
Should juniors use AI tools?
Yes, but with mentorship. Juniors who only generate without understanding plateau early. Use AI as a learning accelerator, not a substitute.
Are there codebases where AI tools should not be used?
Highly regulated (medical, financial), highly sensitive (cryptographic, security), or any context where the cost of subtle bugs is catastrophic. Even then, AI can help with peripheral code.
How is review velocity affected?
Faster generation, slower review. The net is roughly comparable to pre-AI velocity for substantive code.