How to Evaluate AI-Generated Code in Code Reviews
AI-generated code can arrive looking clean, confident, and finished. That is exactly why it needs serious review.
The risk is not that AI always writes bad code. The risk is that it often writes code that looks reasonable before anyone has checked whether it is correct, secure, maintainable, and appropriate for your system.
A good code review in the AI era has to go beyond style comments. It has to test the thinking behind the code.
Start With Ownership
The first question is simple:
Does the engineer who opened the pull request understand the code well enough to own it?
If they cannot explain the approach, trade-offs, failure modes, and test strategy, the review is not ready. AI can help write code, but it cannot be accountable for production behavior. The engineer is still accountable.
That expectation should be explicit on the team. AI-assisted code is not exempt from understanding.
Check the Problem Fit
AI often solves the problem it thinks you asked, not necessarily the problem your product or system actually has.
Ask:
Does this implementation match the real requirement? Did it assume a simpler workflow than the product supports? Does it handle existing business rules? Does it fit with how users actually behave?
This is where domain knowledge matters. A generated solution may pass unit tests and still be wrong for the business.
Look for Missing Edge Cases
AI-generated code commonly misses boundary conditions. Review for:
Empty inputs, duplicate records, retries, timeouts, partial failures, permissions, race conditions, localization, large data sets, backwards compatibility, and migration behavior.
Do not only ask, "Does the happy path work?" Ask, "How does this fail?"
That one question catches a lot.
Review the Tests More Carefully Than the Code
AI can generate tests that mostly confirm its own assumptions. That is dangerous. A large test file is not automatically a strong test suite.
Look for tests that cover behavior, not implementation details. Check negative cases. Check integration points. Check permission and security boundaries. Make sure the tests would fail if the important business logic were wrong.
If the tests only prove that the generated code does what the generated code already does, they are weak tests.
Check Security and Data Handling
Security-sensitive code deserves extra scrutiny. Watch for:
Overly broad permissions, unsafe input handling, secrets in logs, missing authorization checks, insecure defaults, weak validation, and unnecessary exposure of customer data.
AI tools can suggest patterns that are common on the internet but inappropriate for your production environment. Common does not mean safe.
Evaluate Architecture Fit
Generated code may be locally correct while globally wrong.
Ask whether the change fits the existing architecture. Does it introduce a new pattern for no reason? Does it bypass shared abstractions? Does it duplicate business logic? Does it create coupling that will hurt the next project?
Senior reviewers are especially valuable here because they know the history of the codebase. AI does not.
Keep the Review Practical
You do not need to turn every AI-assisted pull request into a courtroom trial. Use judgment. A generated test helper is different from generated authentication logic. A low-risk internal script is different from customer-facing payment code.
Match the review depth to the risk.
A Simple Team Checklist
Before approving AI-assisted code, ask:
- Can the engineer explain the code and trade-offs?
- Does it solve the real product problem?
- Are edge cases and failure modes handled?
- Are tests meaningful and not just generated confirmation?
- Are security, privacy, and permissions correct?
- Does it fit the architecture and team standards?
- Would we be comfortable debugging this in production?
If the answer to any of those is weak, slow down. That is not anti-AI. That is engineering.
For a broader manager perspective, read Leading Engineering Teams in the AI Era. For individual career positioning, read How Senior Engineers Stay Relevant in the AI Era.
About Me
Nimesh Patel is an engineering leader and career coach with over 20 years of experience building cloud-native enterprise and consumer software systems in Big Tech (including Google) and high-growth AI startups. He has led globally distributed engineering organizations of 60+ engineers and leaders, conducted 650+ interviews across engineering, management, and executive roles, made 50+ hires, and coached and promoted 30+ engineers and leaders. He provides interview and career coaching through ScaleYourCareer. Follow him on LinkedIn.
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