Stop Asking AI to Generate Code—Use It to Debug Instead

Alright, here's the thing: every developer I know has tried using AI to write code, and like 60% of the time it's just... mid. But you know what AI is actually good at? Debugging. Like, creepily good.

The Problem With AI Code Generation

When you ask Claude or ChatGPT to write code, you're getting:

• A solution that might fit your constraints
• Boilerplate that needs tweaking
• Zero context about your actual codebase
• A 50/50 chance it works on the first try

When you ask AI to debug? You're giving it something concrete to analyze. Something that failed. Something measurable.

The Debug-First Workflow

Here's what actually works:

Step 1: Get a real error. Run your code. Get a full stack trace. Don't guess at what might be wrong.

TypeError: Cannot read property 'map' of undefined
  at processItems (./api/handlers.js:42:15)
  at async fetchUserData (./api/handlers.js:28:8)

Step 2: Paste the error + minimal context. Not your whole codebase. Just:

• The error message
• The function where it broke
• What you expected to happen

Step 3: Let AI narrow it down. It'll spot the null check you missed or the async/await bug faster than you will.

Real Example: The Async Mess

I had a React component that kept failing on mount:

function UserProfile() {
  const [user, setUser] = useState(null);

  useEffect(() => {
    fetchUser().then(data => {
      setUser(data.profile); // <-- This was throwing
    });
  }, []);

  return <div>{user.name}</div>;
}

The error: Cannot read property 'name' of null

I spent 15 minutes checking the API response. AI spotted it in 10 seconds: "You're rendering user.name before the API returns. The state starts as null."

The fix was obvious once someone pointed it out. But my brain was already in the weeds.

Why This Works Better

1. You own the solution. You wrote the broken code; you understand the fix.
2. AI explains, not generates. It walks you through why it's broken, not just hands you replacement code.
3. You learn faster. Actually debugging teaches you patterns; copy-pasting code teaches you nothing.
4. Your codebase stays yours. No weird generated functions hiding in your project.

The Setup

Make it frictionless:

• Keep error + context in a text editor. Three things: error message, relevant code snippet, what you expected.
• Use AI for the "why." Ask "why does this throw TypeError?" not "fix this code."
• Verify locally before trusting the fix. Even when AI is right, you need to see the fix work.

When NOT to Debug Yourself

Obviously, some bugs are complex:

• Race conditions in distributed systems
• Browser incompatibilities
• Database connection pools
• Anything timing-dependent

For those, yeah, paste more context. AI can help untangle it.

The Real Skill

The actual superpower isn't AI writing code. It's knowing what to ask AI and how to evaluate its answer.

You wouldn't ask a colleague to write your entire feature. You'd say, "Hey, my async handler keeps timing out—what am I missing?"

That's how to use AI. As a rubber duck that talks back.

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Stay sharp. Keep debugging.