AI can write code fast. Shockingly fast. And often, it writes great code.
It’s clean. It compiles. It looks correct. It even comes with tests.
And yet, it can still be subtly wrong, insecure, over engineered, or solving the wrong problem entirely.
Reviewing AI generated code is not the same as reviewing human written code. The failure modes are different. If you review it the same way you review any other teammate’s PR, you’ll miss the real risks.
Start with the most important question: Is this the right solution?
Before you read line by line, it’s important to zoom out.
Ask yourself:
- Does this actually solve the problem we asked?
- Did the AI misinterpret the requirements?
- Do I need to reframe my prompt to clarify what I actually need?
- Is it solving a slightly different problem?
AI can introduce intent drift, solving something adjacent to what you asked.
Always compare the implementation to your original prompt and your intent.
Review for correctness first, not style
AI code is usually stylistically clean. Formatting and naming often look fine.
Instead, focus on edge cases (nulls, empty inputs, time zones, etc.), assumptions (hardcoded values, magic numbers, data shape), and silent failures (catching an exception and ignoring it, or logging nothing meaningful).
Broad exception swallowing can be a red flag.
Do a security pass
Does the security of the code meet your organizational guidelines.
Always be sure to check for things like:
- SQL or command injection
- Input validation
- Hardcoded secrets, or secrets management that does not meet the standards for your project
- Logged tokens
- Authorization checks
Never assume that the model handled secrets correctly
Watch for over engineering
AI loves abstraction.
Be cautions of
- Extra layers
- Unnecessary interfaces
- Factories for simple logic
- Overly generic patterns.
It’s always good to ask yourself, “Would we have done it this way without AI?”
If not, you may want to consider changing or simplifying.
Don’t trust the tests blindly
AI generated tests will often cover the logic it just wrote. Similar to the first point about checking that it is solving for the right solution, ensure that your tests are correct for your business case, not just for the code.
It is also important to check if your tests are:
- Only covering happy paths
- Asserting trivial behavior
- Properly mocking
- Checking for failure cases
Ask yourself while reviewing your tests, “Would this test suite catch a real bug”
Verify APIs and Libraries
APIs change all the time. New and deprecated end points. Different versions. Multiple libraries that may perform similar functions.
Always be sure to validate with official docs, and never merge unverified external calls
Review for maintainability
While your future development may also be AI assisted, you still need to make sure the code is understandable.
Be sure the check for things like:
- Are function and variable names meaningful?
- Are comments explaining “why” instead of “what”?
- Does the code follow my codebase and organization conventions?
- Would someone understand this code in 6 months?
The real risk is intent drift
The biggest danger of AI generated code isn’t messy syntax.
It’s subtle logic drift from what you intended
Instead of asking, “Is this code clean?”
Ask yourself, “Is this behavior correct? Does this meet my requirements?”
AI accelerates implementation. It does not replace engineering judgement.
Your review process is where that judgement lives.
Conclusion
AI can write code.
It cannot own the consequences of shipping it
Companies that win with AI won’t be the ones generating the most lines of code, they will be the ones who combine AI speed with human judgement.
Implementing AI well is a competitive advantage.
Doing it poorly is a liability.
Comments
Post a Comment