Debugging AI-Generated Code

AI-generated code has a unique failure mode: it looks perfectly reasonable but doesn't work. Here's a systematic approach to finding and fixing these bugs.

Common AI Code Bugs

1. Phantom APIs and Methods

AI models sometimes "hallucinate" methods that don't exist. They'll call array.findLast() in an environment that doesn't support it, or use a library function with the wrong signature.

How to spot it: Look for method calls you've never seen before. Check the documentation.

// AI wrote this — looks fine, but .toSorted() might not exist
// in your Node.js version
const sorted = items.toSorted((a, b) => a.date - b.date);

// Safe alternative
const sorted = [...items].sort((a, b) => a.date - b.date);

2. Wrong Import Paths

AI frequently guesses import paths, especially in monorepos or projects with custom aliases.

// AI guessed this path
import { db } from "@/lib/database";

// But your actual file is
import { db } from "@/lib/db";

Fix: Always verify imports match your actual file structure.

3. Async/Await Mistakes

AI code often forgets await on async operations, or adds async to functions that don't need it. The code runs without errors but produces unexpected results.

// Bug: missing await — user will be undefined
function getUser(id: string) {
  const user = db.user.findUnique({ where: { id } });
  return user; // Returns a Promise, not a User
}

// Fix
async function getUser(id: string) {
  const user = await db.user.findUnique({ where: { id } });
  return user;
}

4. Stale Closure Values

In React components, AI-generated event handlers often capture stale state values.

// Bug: count is always 0 inside the interval
const [count, setCount] = useState(0);

useEffect(() => {
  const interval = setInterval(() => {
    setCount(count + 1); // Always 0 + 1 = 1
  }, 1000);
  return () => clearInterval(interval);
}, []);

// Fix: use functional update
setCount(prev => prev + 1);

The Debugging Process

Step 1: Read the Error

Sounds obvious, but AI-generated code produces errors you didn't write, so you might skim them. Read the full error message and stack trace. The line numbers point you directly to the problem.

Step 2: Isolate the Failure

If a whole feature is broken, comment out sections until you find the specific line that fails. AI tends to produce all-or-nothing code blocks — narrowing down the problem is your job.

Step 3: Check Types at Runtime

AI-generated TypeScript often has types that don't match runtime reality. Add console.log(typeof variable, variable) at key points to verify.

// AI says this returns User[], but does it really?
const users = await getUsers();
console.log("users:", typeof users, Array.isArray(users), users);

Step 4: Ask AI to Fix It

Once you've identified the bug, the most effective debugging prompt is:

This code throws [exact error message] on line [X].
Here's the relevant code: [paste code].
Here's what I expected: [behavior].
Here's what actually happens: [behavior].
Fix the bug.

Giving the AI the error message and both expected/actual behavior dramatically improves the fix quality.

Prevention Strategies

Use TypeScript Strictly

Enable strict: true in your tsconfig.json. This catches type mismatches, null reference errors, and missing return types at compile time — before they become runtime bugs.

Test Each Prompt's Output

Don't stack 5 prompts of generated code and then test. Generate, test, commit. Generate, test, commit. Finding a bug in 20 lines is easy. Finding it in 200 is painful.

Keep AI Context Fresh

If you've made manual changes to your code, paste the updated version back to the AI before asking for the next feature. Stale context is the #1 cause of integration bugs.

Tools That Help

• Vibe Checker — Automated code review that catches common AI bugs
• Code Explainer — Understand what AI-generated code actually does before running it
• Browser DevTools — Network tab for API issues, Console for runtime errors
• TypeScript compiler — Your first line of defense against type bugs