AI can generate code incredibly fast.
Sometimes fast enough to make you pause and think, “Did this just replace half my work?”
But speed has never been the hard part of engineering.
The real challenge is making sure the code:
- does the right thing,
- fails safely,
- can be understood later,
- and won’t hurt you in production.
That’s why reviewing AI-written code requires a slightly different mindset than reviewing human code. The mistakes are subtler, the confidence is misleading, and the risks are easy to underestimate.
This post walks through how engineers actually review AI-generated code in real projects — not in demos, not in theory.
Start With the Right Mindset
The most important rule is simple:
Treat AI-written code as a first draft, not a solution.
It may compile.
It may pass basic tests.
It may even look clean.
That doesn’t mean it’s correct, safe, or appropriate for your system.
engineers don’t ask “Does this work?”
They ask “Should this exist in this form?”
Step 1: Understand the Intent Before the Code
Before reading the implementation, step back and ask:
- What problem is this code supposed to solve?
- What are the inputs and expected outputs?
- What guarantees does it claim to provide?
This matters because AI often solves the prompt, not the real requirement.
If you can’t explain the intent clearly in your own words, reviewing the code line by line is pointless. Misaligned intent leads to clean-looking but wrong solutions.
Step 2: Think About Failure, Not Success
AI code usually handles the happy path well.
That’s not where production issues come from.
Ask questions like:
- What happens with empty or invalid input?
- What happens under load?
- What happens when a dependency slows down or fails?
- What happens if this 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.
Step 3: Look for Silent Assumptions
AI is very good at making assumptions — and very 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
Any time the logic depends on something always being true, stop and verify where that guarantee comes from. Many real production bugs are just assumptions that stopped being true over time.
Step 4: Review Security Before Style 🔐
Clean code doesn’t matter 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 often looks 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.
Step 5: Check the Level of Complexity
AI tends to either:
- over-engineer simple problems, or
- under-engineer complex ones.
Look for:
- unnecessary abstractions,
- premature generalization,
- hardcoded behavior that limits future change.
Ask yourself: Is this complexity solving a real problem today, or just adding mental overhead?
engineers value clarity over cleverness.
Step 6: Read It Like You’ll Own It Long-Term
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.
Step 7: Check Observability
When this code fails in production, how will you know?
Look for:
- 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.
Step 8: Don’t Trust Without Tests
Never approve AI-written code without tests.
Check whether tests:
- cover edge cases,
- assert behavior instead of implementation,
- fail for the right reasons.
Tests are not just about correctness — they’re future documentation for how this code is expected to behave when things go wrong.
Step 9: Decide What to Keep and What to Rewrite
engineers don’t treat AI output as all-or-nothing.
Often the best outcome is:
- keep the structure,
- rewrite critical logic,
- simplify what doesn’t need to be clever.
AI is a productivity accelerator, not an authority. You’re still responsible for the final shape of the code.
🔗 Read the full write-up on reviewing AI-written code like a engineer
A Practical Rule of Thumb
Many experienced engineers follow something like this:
- AI for scaffolding → ✅
- AI for non-critical logic → ⚠️
- AI for security-sensitive paths → ❌ without deep review
The more critical the code, the more human ownership it requires.
Why This Skill Matters More Every Year
As AI writes more code:
- codebases grow faster,
- context becomes thinner,
- 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.”
That’s judgment.
Final Thoughts
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.
Question it calmly.
Improve it deliberately.
AI can help you write code faster.
Engineering judgment is what makes it safe.
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