The challenge of AI-generated code
AI tools like Cursor and Claude Code generate increasingly better code, but "better" does not mean "perfect." AI-generated code can look correct at first glance, pass all tests, and still contain fundamental problems. From security vulnerabilities to performance bottlenecks, from unnecessarily complex solutions to subtle logical errors — the challenges are real and require a thoughtful approach to code review.
The problem is amplified because AI code is often convincing. It follows naming conventions, contains comments, and looks professional. This can lead to a false sense of confidence where developers review less critically than with handwritten code. An effective review strategy for AI code is therefore essential for any team that takes AI development seriously.
Recognizing common problems
Overengineering
AI tends to generate solutions that are more complex than necessary. A simple data transformation sometimes becomes a full design pattern with abstraction layers that are unnecessary for your use case. Be alert to code that looks impressive but does not simplify the problem.
Security blind spots
AI models are trained on public code, including examples with security issues. Input validation, SQL injection prevention, and XSS protection are sometimes forgotten or incompletely implemented. Check every external input processing extra carefully.
Hallucinations in API usage
AI can call APIs that do not exist or use parameters that are not supported. This occurs especially with less popular libraries or more recent API versions. Always verify that the functions and methods used are actually available.
Never blindly trust AI-generated code, no matter how correct it looks. The combination of automated tests, human review, and security scanning forms the foundation of reliable AI development.
A review framework for AI code
At Breathbase, we use a structured review framework for AI-generated code. Each review goes through four phases: functionality (does the code do what was asked?), security (are there security risks?), maintainability (is the code readable and understandable?), and performance (are there unnecessary computations or memory usage?).
Always start by running existing tests. If the code adds new functionality, write additional tests first before reviewing the implementation. This gives you an objective framework to evaluate the code against. Then do a manual walkthrough of the logic, checking every assumption and edge case.
Tools and automation
Automate as much of the review process as possible. Set up linters that automatically detect style violations. Use static analysis tools like SonarQube or ESLint with security plugins. Integrate these tools into your CI/CD pipeline so every commit is automatically checked, regardless of whether the code was written by a human or AI.
Also consider AI-powered review tools specifically designed to detect problems in AI-generated code. These tools recognize patterns typical of AI output, such as unnecessary abstractions, outdated API calls, and inconsistencies with your project standards.
Quality as a culture
Ultimately, code quality is a team responsibility. Regardless of the source of the code — human or AI — the same standards apply. Invest in AI development training so your team learns how to review effectively, and build a culture where quality takes priority over speed. The combination of AI development tools and a strong review process delivers the best results.
