The reality of being a developer is that we often spend more time fixing code that actually writing new code.

Debugging is such a core and time-consuming part of software development, it’s no surprise that most developers now use some fomr of artificial intelligence, like IDE-embedded LLMs like GitHub Copilot, Cursor or other such products.

It’s not always plain-sailing, however, with any users in our community saying that while they find AI to be hugely useful, becoming too reliant on it is a fool’s errand, and developers need to be careful not to fall into AI’s traps.

In this article, we’ll help you navigate its limitations and avoid common pitfalls when relying on AI for debugging your code.

AI Debugging: What It Can (and Can’t) Do

AI-driven debugging tools leverage large datasets and pattern recognition to suggest fixes for bugs, optimise performance, and even detect vulnerabilities.

While these are hugely useful, it’s important to remember AI does not “understand” code in the way a human does — it recognises patterns and makes predictions based on training data, not because of an in-depth and specific knowledge of your project and how it’s built.

This means that while it can surface likely issues, it may also introduce new ones if developers accept AI-generated suggestions without scrutiny.

Getting Better Results from AI Debugging Tools

Despite being highly-skilled, and often having a fair understanding of how LLMs operate, developers use them in surprisingly simplistic ways.

Most of the time, we paste in a block of code, ask a simple question and cross our fingers, hoping the LLM will fix our code and we can move on.

However, there really is a craft to prompting your LLM for the best response.

Be Explicit About the AI’s Role

AI performs better when given clear instructions, however there[s a little more to it than that.

It also seemingly performs better when it knows what role it is playing, and what knowledge base it has.

Think of it like this… if I ask a question to my mother about a legal situation, she would give me very different advice to a lawyer. So if I tell the LLM its a lawyer, it’ll behave like one.

So, here’s an example… “You are an expert JavaScript developer. Analyze this React component and explain why the state is not updating as expected.”

Use AI as an Assistant, Not a Replacement

AI can generate quick solutions, but always verify its suggestions. Think of it as a second pair of eyes rather than an absolute authority.

Blindly copying and pasting AI-generated fixes can introduce subtle errors. Always test solutions and review AI’s logic to ensure it aligns with project requirements.

Iterate and Refine AI Queries

If the first response isn’t helpful, refine your query instead of assuming AI can’t solve the problem. Add more context, specify programming languages, or include example inputs/outputs.

All of this helps the LLM to understand your project, what you are expecting from it, the specific issues you have encountered, and what you have tried up to this point. It’s really not that different to what Stack Overflow commenters expected from us, right?

Traps to Avoid When Debugging with AI

While AI offers powerful debugging capabilities, developers should be aware of potential traps:

1. Over-Reliance on AI

AI-generated solutions can sometimes be incorrect or inefficient. Relying too much on AI can lead to complacency, where developers trust suggestions without verifying them.

2. Ignoring the Fundamentals

AI is a tool, not a crutch. Developers should still build strong debugging skills—understanding stack traces, using breakpoints, and writing meaningful tests.

3. Not Considering AI’s Limitations

AI is trained on public data and may not be aware of your project’s architecture, business logic, or internal dependencies. Always tailor AI’s suggestions to fit your specific context, and remember it will find ways to make simple mistakes, so you need to watch out for these.

4. Failing to Debug AI’s Debugging

If an AI-generated fix doesn’t work, treat it like any other bug. Debug its solution, check its logic, and refine your approach from there.

Conclusion

AI-powered debugging is already reshaping how developers approach error resolution.

By using AI wisely, framing queries effectively, verifying suggestions, and avoiding common traps, developers can enhance their debugging process without compromising code quality.

The future of AI in software development isn’t about replacing developers but refining how they work, making debugging faster, more efficient, and more intelligent.