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How to Debug AI-Generated Code: A Practical Guide

Cowrie
Cowrie
Dev @ Bswen

Problem

I asked Claude to generate a user authentication module. The code compiled. Tests passed. I deployed it to staging.

Then my security scanner flagged three critical vulnerabilities: missing token expiration, SQL injection risk, and hardcoded secrets in error messages.

The code looked correct. It ran correctly. But it was dangerously wrong.

This is the core challenge of AI-generated code: it looks right until it fails catastrophically.

What Makes AI Code Hard to Debug

When I write code myself, I understand every decision. When AI generates code, I’m reverse-engineering not just bugs but intent itself.

Confident Wrongness

LLMs excel at producing plausible-looking code. It compiles. It runs. But it has subtle logic bugs, missing edge cases, or security holes.

// AI-generated code that looks correct
async function processUserData(userId: string) {
  const user = await fetchUser(userId);
  const orders = await fetchOrders(userId);


  return {
    ...user,
    orders: orders,
    totalSpent: orders.reduce((sum, o) => sum + o.amount, 0)
  };
}

At first glance, this looks fine. But I found four issues:

  1. No validation of userId
  2. No error handling for failed fetches
  3. No handling for missing user
  4. amount might not exist on all orders

Integration Blind Spots

AI generates code in isolation. It doesn’t know your database schema, your authentication flow, or your error handling patterns. Functions work individually but fail when integrated.

Inconsistent Quality

The same AI can generate brilliant code for one component and problematic code for another. Error handling might be robust in one function and nonexistent in another.

The Debugging Process I Use

After debugging dozens of AI-generated modules, I developed a systematic approach.

Step 1: Document Expected Behavior

Before looking at the code, I write down what it should do:

## Expected Behavior for processUserData
- Accept userId string, validate format
- Fetch user, handle missing user case
- Fetch orders, default to empty array
- Calculate total spent with null safety
- Return structured result or throw typed errors

This prevents me from “fixing” code to match broken behavior.

Step 2: Run Static Analysis

I run multiple analysis layers:

Terminal window
# Layer 1: Standard linters
npm run lint
npm run type-check


# Layer 2: Security scanners
npm audit
npx snyk test


# Layer 3: AI-specific analysis
npx vibecheck analyze ./src

I document every warning, even ones I plan to ignore. Patterns emerge across files.

Step 3: Identify the Gap

With expected behavior documented and static analysis complete, I compare what should happen versus what the code actually does.

Tools like vibe.rehab help here. I paste in the broken code and describe what it should do. The tool identifies discrepancies:

Input: [broken AI code] + [expected behavior description]
Output: [identified issues] + [corrected code] + [explanation]

Step 4: Isolate and Test

I create minimal test cases rather than debugging the full module:

// Instead of debugging 500 lines, isolate the problem
describe('processUserData', () => {
  it('should throw ValidationError for invalid userId', () => {
    expect(() => processUserData('')).toThrow(ValidationError);
    expect(() => processUserData(null)).toThrow(ValidationError);
  });


  it('should throw NotFoundError for missing user', async () => {
    mockFetchUser.mockResolvedValue(null);
    await expect(processUserData('valid-id')).rejects.toThrow(NotFoundError);
  });


  it('should handle orders with missing amounts', async () => {
    mockFetchUser.mockResolvedValue({ id: '1', name: 'Test' });
    mockFetchOrders.mockResolvedValue([
      { id: '1', amount: 100 },
      { id: '2' }, // Missing amount
      { id: '3', amount: null }
    ]);


    const result = await processUserData('valid-id');
    expect(result.totalSpent).toBe(100); // Only count valid amounts
  });
});

Step 5: Fix Incrementally

I fix issues one category at a time, testing after each batch:

  1. Syntax and type errors - Get it compiling
  2. Logic errors - Make it work correctly
  3. Error handling - Make it robust
  4. Security issues - Make it safe
  5. Performance - Optimize if needed

Here’s the fixed version of my earlier example:

// Fixed version with proper error handling
async function processUserData(userId: string): Promise<UserDataResult> {
  // Validation
  if (!userId || typeof userId !== 'string') {
    throw new ValidationError('Invalid userId');
  }


  // Fetch user with error handling
  let user: User;
  try {
    user = await fetchUser(userId);
  } catch (error) {
    throw new UserFetchError(`Failed to fetch user: ${userId}`, error);
  }


  // Handle missing user
  if (!user) {
    throw new NotFoundError(`User not found: ${userId}`);
  }


  // Fetch orders with fallback
  const orders = await fetchOrders(userId) ?? [];


  // Safe calculation with type guards
  const totalSpent = orders.reduce((sum, order) => {
    const amount = order?.amount ?? 0;
    if (typeof amount !== 'number') {
      console.warn(`Invalid amount in order: ${order?.id}`);
      return sum;
    }
    return sum + amount;
  }, 0);


  return { ...user, orders, totalSpent };
}

Step 6: Document Learnings

After fixing, I record what went wrong:

## Debug Log: User Authentication Module


**Date**: 2026-03-22
**AI Tool**: Claude 3.5
**Original Prompt**: "Create user authentication with JWT tokens"


### Issues Found


1. **Missing Token Expiration** (High)
   - Location: `auth.js:45`
   - Problem: JWT tokens had no expiration
   - Fix: Added `expiresIn: '24h'` to sign options


2. **SQL Injection Vulnerability** (Critical)
   - Location: `user-repository.js:23`
   - Problem: User input interpolated into query
   - Fix: Parameterized query with prepared statements


### Root Causes


1. Prompt didn't specify security requirements
2. Prompt didn't mention token expiration
3. AI defaulted to simple examples without production safeguards


### Improved Prompt


\`\`\`
Create user authentication with JWT tokens for production use.
Requirements:
- Tokens expire after 24 hours
- All database queries use parameterized statements
- Input validation on all user-provided data
- Rate limiting on authentication endpoints
- Secure password hashing with bcrypt (cost factor 12)
- Comprehensive error handling with user-friendly messages
\`\`\`


### Time Spent
- Static analysis: 5 minutes
- Manual review: 15 minutes
- Fixing issues: 30 minutes
- Testing: 20 minutes
- **Total**: 70 minutes (vs. ~180 minutes to write from scratch)

Tools for AI Code Analysis

I’ve found these tools particularly useful:

Traditional Static Analysis

Terminal window
# JavaScript/TypeScript
npm run lint          # ESLint
npm run type-check    # TypeScript strict mode


# Python
pylint src/
mypy src/ --strict


# Security
npm audit
snyk test

AI-Specific Tools

vibecheck.expert combines static analysis with AI insights. It catches patterns traditional tools miss:

Terminal window
npx vibecheck expert ./src --output report.json

vibe.rehab takes broken AI code plus your intent description, then returns working code with explanations:

Terminal window
vibe-rehab fix ./broken_code.py \
  --intent "Process user registration, validate email, create account" \
  --output ./fixed_code.py

Stageclear.dev is a CLI security scanner targeting AI-generated code vulnerabilities:

Terminal window
npx stageclear scan ./src

Common Mistakes to Avoid

Trusting Without Testing

“It compiles, so it must be fine.”

AI code often compiles while containing logical errors. I write tests before trusting any AI output.

Fixing Without Understanding

Changing code until it works, without understanding why it was broken, leads to fragile fixes. The bug comes back differently next time.

I always trace the root cause:

// Broken code
const total = orders.reduce((sum, o) => sum + o.amount, 0);


// Understanding: amount might be undefined
// Root cause: AI didn't handle missing data case
// Fix: Add null safety
const total = orders.reduce((sum, o) => sum + (o.amount ?? 0), 0);

Skipping Security Analysis

Traditional linters miss AI-specific vulnerabilities. AI code might use outdated APIs, skip input validation, or expose sensitive data.

// AI generated this - looks fine
app.get('/user/:id', async (req, res) => {
  const user = await db.query(`SELECT * FROM users WHERE id = ${req.params.id}`);
  res.json(user);
});


// Security scanner caught this:
// SQL injection via interpolated parameter
// Fixed:
app.get('/user/:id', async (req, res) => {
  const user = await db.query('SELECT * FROM users WHERE id = $1', [req.params.id]);
  res.json(user);
});

Over-Correcting

Some developers rewrite AI code entirely, losing time savings. I find AI code gets 80-90% right. Targeted fixes are faster than wholesale rewrites.

Automation for AI Code Quality

I set up a CI pipeline specifically for AI-generated code:

.github/workflows/ai-code-review.yml
name: AI Code Review Pipeline


on: [push, pull_request]


jobs:
  static-analysis:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Run linters
        run: |
          npm run lint
          npm run type-check


      - name: Security scan
        run: |
          npm audit
          npx snyk test


      - name: AI-specific analysis
        run: |
          npx vibecheck expert ./src --output report.json


      - name: Upload results
        uses: actions/upload-artifact@v3
        with:
          name: analysis-report
          path: report.json

Summary

Debugging AI-generated code requires a different mindset. You’re not just fixing bugs—you’re understanding intent, identifying gaps between expectation and implementation, and building systems to catch issues early.

Key practices I follow:

  1. Clarify intent first—Know what the code should do before fixing what it does
  2. Use AI-specific tools like vibecheck.expert alongside traditional static analysis
  3. Test comprehensively—Unit tests, integration tests, and edge cases
  4. Document learnings to improve future AI interactions
  5. Fix incrementally rather than rewriting wholesale

The goal isn’t to avoid AI-generated code, but to develop workflows that catch issues early. With the right tools and process, debugging AI code becomes faster than writing from scratch.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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