How to Review AI-Written Code Like a Engineer

Source: Dev.to

Introduction

AI can generate code incredibly fast, but speed has never been the hard part of engineering.
The real challenge is ensuring the code:

• does the right thing,
• fails safely,
• can be understood later,
• won’t hurt you in production.

Reviewing AI‑written code requires a slightly different mindset than reviewing human code. The mistakes are subtler, confidence can be misleading, and the risks are easy to underestimate.

Treat AI‑Written Code as a First Draft

• It may compile, but that doesn’t mean it’s correct, safe, or appropriate.
• Engineers should ask, “Should this exist in this form?” rather than simply “Does this work?”

Before diving into the implementation, step back and clarify:

1. What problem is this code supposed to solve?
2. What are the inputs and expected outputs?
3. What guarantees does it claim to provide?

If you can’t explain the intent in your own words, line‑by‑line review is pointless. Misaligned intent leads to clean‑looking but wrong solutions.

Edge Cases and Failure Modes

AI code usually handles the happy path well. Probe the less obvious scenarios:

• What happens with empty or invalid input?
• What happens under load?
• What if a dependency slows down or fails?
• What if the function is called in an unexpected order?

Engineers always look for failure modes first. If the code has no obvious way to fail, that’s usually a bad sign.

Hidden Assumptions

AI is good at making assumptions and bad at documenting them. Common ones include:

• “This value is never null.”
• “This list is always sorted.”
• “This function is only called once.”
• “This service is always fast.”

Whenever logic depends on something always being true, stop and verify where that guarantee comes from. Many production bugs stem from assumptions that stop being true over time.

Security Considerations

Clean code is irrelevant if it’s unsafe. Before commenting on formatting or naming, check:

• Are authorization checks present?
• Is user input validated?
• Is sensitive data protected?
• Are defaults safe?

AI‑generated code can look professional while quietly skipping security boundaries. If the code touches authentication, payments, or user data, it deserves extra scrutiny. Security issues are expensive because they don’t always fail loudly.

Complexity: Over‑Engineering vs. Under‑Engineering

AI tends to either:

• Over‑engineer simple problems, or
• Under‑engineer complex ones.

Look for:

• Unnecessary abstractions,
• Premature generalization,
• Hard‑coded behavior that limits future change.

Ask yourself: Is this complexity solving a real problem today, or just adding mental overhead?

Readability and Maintainability

A useful mental trick is to ask:

• Would I be comfortable debugging this at 2 AM?
• Would a new teammate understand this without context?
• Are names honest and specific?
• Are side effects obvious?

AI code often uses generic naming and hides behavior in ways that feel fine at first but become painful later. If something feels hard to reason about now, it will only get worse with time.

Logging and Observability

When the code fails in production, how will you know? Ensure the presence of:

• Meaningful logs,
• Useful error messages,
• Signals that point to the real problem.

Silent failures are dangerous. Good code doesn’t just fail—it explains why.

Testing

Never approve AI‑written code without tests. Verify that tests:

• Cover edge cases,
• Assert behavior instead of implementation,
• Fail for the right reasons.

Tests serve as future documentation for how the code is expected to behave when things go wrong.

Practical Review Workflow

Engineers don’t treat AI output as all‑or‑nothing. Often the best outcome is:

1. Keep the overall structure.
2. Rewrite critical logic.
3. Simplify anything that doesn’t need to be clever.

AI is a productivity accelerator, not an authority. You remain responsible for the final shape of the code.

Risk Matrix

The more critical the code, the more human ownership it requires.

Conclusion

As AI writes more code, codebases grow faster, context becomes thinner, and risk increases quietly. The engineers who stand out won’t be the ones who generate the most code; they’ll be the ones who can look at confident‑looking solutions and say, “This feels right, but it’s actually wrong.”

AI doesn’t remove responsibility—it concentrates it. If you approve AI‑written code, you own it in production, during incidents, in audits, and in postmortems. Review it carefully.

🔗 [Read the full write‑up on reviewing AI‑written code like an engineer]