Tools like GitHub Copilot, Cursor, and ChatGPT are completely changing the way we write code. They speed up development, reduce repetitive work, and make life easier for devs. But there’s a big catch: the quality of AI-generated code isn’t always reliable. And worse, the mistakes it makes can be very different from what a human would typically do.
A study from Stanford University found that developers using AI-powered coding assistants are more likely to introduce security vulnerabilities compared to those writing code manually. That’s because AI-generated code often looks correct but can hide flaws that become major issues in production.
So, how can we take advantage of AI without falling into these traps? Let’s break down the biggest challenges and how to handle them.
AI Creates Bugs You Wouldn’t Expect
If you think AI makes the same mistakes as a human developer, think again. Studies show that AI-generated code has unique bug patterns that can be tricky to catch—until they cause real problems in production.
- Misinterpretation – AI can misinterpret the prompt, generating code that looks right but doesn’t actually solve the intended problem. This often happens due to ambiguous input or model limitations.
- Syntax Errors – Simple syntax mistakes, like unclosed parentheses or unfinished strings, can happen when AI generates long and complex structures.
- Silly Mistakes – Redundant checks, unnecessary type conversions, and inefficient control structures can creep in, making the code bloated and inefficient.
- Prompt-Biased Code – AI-generated code can be overly dependent on the prompt, leading to solutions that don’t generalize well to other use cases.
- Missing Corner Cases – Since AI doesn’t test the code before suggesting it, edge cases and error handling can be completely overlooked.
- Wrong Input Type – Incorrect data types in functions due to faulty inference of expected parameters or return values.
- Hallucinated Objects – AI might reference non-existent libraries, methods, or classes, especially if its training data had fragmented or inconsistent examples.
- Wrong Attribute – AI can call attributes that don’t actually exist in an object, leading to runtime errors.
- Incomplete Generation – Some AI-generated code gets cut off, leaving functions unfinished, loops incomplete, or conditionals without execution blocks.
- Non-Prompted Consideration – AI might add unnecessary logic or performance-impacting code that wasn’t requested.
If these issues aren’t caught early, they can cause critical failures and increase debugging time. That’s why automated testing and thorough code reviews are essential.
AI Doesn’t Understand Your Project’s Context
Experienced developers don’t just write code to fix an immediate issue; they consider system architecture, scalability, security, and maintainability. Every line of code needs to fit into the existing infrastructure and support the product’s evolution.
AI, however, doesn’t have this contextual understanding. It generates code based on patterns from massive datasets without considering your project’s specific requirements. That means AI-generated solutions might not align with security policies, regulatory compliance, or performance needs. Blindly trusting AI-generated code can introduce vulnerabilities, bottlenecks, or incompatibilities with your application’s ecosystem.
AI vs. Human Developers
AI models are trained on vast amounts of data and can generate code quickly, but that doesn’t mean they understand the unique requirements of your project. A human developer doesn’t just write code—they analyze requirements, assess architecture, evaluate risks, and make decisions based on experience and context.
For example, a system might have strict security rules that weren’t mentioned in the prompt. No matter how advanced AI is, it only generates code based on learned patterns, without understanding those implicit requirements. This can lead to incomplete or even dangerous solutions.
Code Review Needs to Adapt to AI
If code review was important before, it’s now critical. AI-generated code requires a different kind of scrutiny to catch errors that aren’t always obvious.
How to Improve Code Reviews
Code review should be more than just a quick scan. It needs to ensure that AI-generated code is secure, scalable, and consistent with project standards. Here’s how:
- Check if the code actually does what was requested. AI can misinterpret prompts and generate something that looks right but doesn’t solve the problem effectively.
- Test different scenarios, including edge cases. AI-generated code might work for the main use case but fail in unexpected situations.
- Ensure the code follows project best practices. Even if AI generates functional code, it might not adhere to your team’s design and architecture standards, making maintenance harder.
Automating the Process
With the increasing volume of AI-generated code, relying solely on manual reviews is no longer enough. Automated code analysis is essential to maintain quality and security at scale.
Automation allows for continuous validation of security standards, best practices, and project requirements without depending entirely on human reviews. This improves team efficiency and reduces time spent on lengthy manual reviews.
You can use Kodus to automate your code review process.
Recommended Reads:
Conclusion
AI-generated code can save time, but it also introduces new challenges. To avoid issues, follow these best practices:
- Watch out for common AI-generated bugs.
- Always review and test AI-generated code before using it.
- Use automated tools to catch errors early.
- Don’t rely entirely on AI—always understand what’s being generated.
If your team is using AI to generate code, it’s worth adapting your review processes now to prevent future headaches.
