Why Debugging is Your Superpower When Coding with AI

AI coding assistants like GitHub Copilot are game-changers. They generate boilerplate, autocomplete functions, and sometimes even solve complex problems with a few prompts. But here’s the thing no one tells you until you’re knee-deep in a half-broken feature: AI can’t replace your brain — especially when it comes to debugging.

In this post, I want to talk about something every developer using AI tools will face sooner or later — the trap of relying too much on AI, and the critical role debugging plays in making that AI-generated code actually work.

AI Can Write Code, But It Doesn’t Understand Your App

AI doesn’t truly understand your application’s context. It predicts code based on patterns it’s seen in massive codebases, but it doesn’t know what your app is trying to do, or how your state flows, or why you structured things a certain way.

When you ask Copilot to fix a bug, it can suggest a change that seems reasonable in isolation but ends up breaking something else. That’s because it doesn't remember the reason behind your current logic — only the pattern you're asking it to fix.

Debugging Is How You Take Back Control

At first, it feels easy to just let the AI fix things. But as bugs start to stack and features start behaving weirdly, you realize: you need to understand what’s actually going on.

Debugging — stepping through the logic, adding "console.log()" or "alert()" statements, watching how state updates — is the only way to truly get back in the driver’s seat.

Once you understand the real flow and the root cause of a bug, you can start guiding the AI with much more precision. You’re no longer blindly accepting suggestions — you’re using the AI as a powerful assistant, not a driver.

The Danger of Chasing Bugs with Prompts Alone

One common trap is trying to fix one bug at a time using the AI, only to realize that each "fix" creates two new issues. That’s because you’re patching symptoms without understanding the underlying system.

AI doesn't have memory of your bug history or why you coded a workaround in the first place. If you change something now, it won’t warn you that it will undo a previous fix — that responsibility is yours.

The better approach? Pause. Read the code. Follow the data. Step into the logic. Then ask the AI to refactor or optimize based on your understanding.

Understanding First, Prompting Second

The real productivity boost comes when you use AI after you’ve debugged and understood the flow. At that point, your prompts become smarter:

Instead of saying: “Fix this delete bug”
You say: “When I delete a file from the list, update the file index so the UI re-renders correctly without breaking the reference to existing files.”

That level of specificity gets you way better results. AI is great at turning known logic into code — not discovering the logic for you.

Your Brain is Still the Best Debugger

AI might be fast, but your brain still has something it doesn’t — context, reasoning, and memory of your project’s evolution.

When you take the time to understand the problem, trace the data, and test edge cases manually, you’ll unlock better performance not just from your code, but from your AI assistant as well.

Final Thoughts

Using AI for code generation is like pair programming with a hyper-productive, slightly forgetful partner. You can go far together — but only if you’re actively reviewing, debugging, and steering the ship.

So next time Copilot writes half your feature, don’t skip the old-school stuff. Debug hard, think deeply, and treat the AI like a power tool — not a replacement for your own engineering skills.