Debugging AI-Generated Code: Expert Tips with Cursor and Claude Sonnet

Why AI-Generated Code Needs Different Debugging

Debugging AI-generated code isn't the same as debugging code you wrote yourself. When Claude Sonnet or Cursor generates code, you're dealing with:

• Black box generation: You didn't see the code being written line by line
• Unfamiliar patterns: AI may use different approaches than you would
• Hidden assumptions: The AI made decisions you weren't aware of
• Context limitations: AI doesn't always have your full project context

Setting Up Your Debugging Environment

Enable Cursor Debug Mode

Before you start debugging, configure Cursor for maximum visibility:

# Enable verbose logging
export ANTHROPIC_LOG=debug

# Or use the debug flag
cursor --debug

# View logs in real-time (macOS)
tail -n 20 -F ~/Library/Logs/Claude/mcp*.log

Configure Claude Desktop Developer Tools

Enable Chrome DevTools within Claude Desktop to inspect message payloads and catch client-side errors:

1. Open Claude Desktop settings
2. Enable "Developer Mode"
3. Right-click anywhere and select "Inspect Element"
4. Navigate to Console tab for error messages

The Systematic Debugging Workflow

Follow this proven workflow to debug AI code efficiently:

Step 1: Reproduce the Error Reliably

You can't fix what you can't reproduce. Create a minimal test case:

1. Isolate the problematic code: Remove everything except what's needed to trigger the error
2. Document exact steps: Write down the exact sequence that causes the issue
3. Check environment: Does it fail in dev, prod, or both?
4. Verify inputs: What data or state triggers the problem?

Step 2: Read and Understand AI-Generated Code

Don't assume AI code does what you think it does. Actually read it:

• Check variable names - do they make sense?
• Verify function signatures match your expectations
• Look for edge cases the AI might have missed
• Identify any hard-coded values or assumptions

Step 3: Use AI to Explain AI Code

One of the most powerful debugging techniques: ask Claude to explain its own code.

Common AI Code Errors and How to Fix Them

1. Type Mismatches in TypeScript

The Problem: AI generates code with incompatible types or missing type definitions.

The Fix:

• Use @linter-errors to surface all type issues
• Ask Claude: "Fix all TypeScript type errors in this file"
• Provide type definitions for external dependencies
• Use any temporarily to isolate the real bug

2. Async/Await Race Conditions

The Problem: AI code doesn't properly handle async operations, leading to race conditions.

const data = fetchData();
console.log(data); // undefined!

const data = await fetchData();
console.log(data); // Works!

3. Environment Variable Errors

The Problem: Code works locally but fails in production due to missing environment variables.

The Fix:

1. Use --debug mode to see exactly which env vars are accessed
2. Check your .env.example matches production
3. Add runtime checks: if (!process.env.API_KEY) throw new Error()
4. Use a validation library like zod for env vars

Advanced Debugging Techniques

Use MCP Inspector for Deep Debugging

MCP (Model Context Protocol) Inspector lets you debug the AI's interaction with your codebase:

• See exactly what context the AI received
• Inspect tool calls and responses
• Identify context truncation issues
• Debug MCP server implementations

Reset Context Strategically

When Claude gets confused, use /Reset Context but provide fresh, accurate context:

Debugging Workflow Example: Real Case Study

Let's walk through debugging a real issue:

if (!process.env.DATABASE_URL) {
  throw new Error('DATABASE_URL is required');
}

Best Practices for Error-Free AI Code

Write Better Prompts

Prevention is better than cure. Write prompts that result in debuggable code:

• ✅ "Add error handling for network failures"
• ✅ "Include TypeScript types for all function parameters"
• ✅ "Add console.log statements for debugging"
• ❌ "Make it work" (too vague)

Review Before Accepting

Don't blindly accept AI code. Check:

1. Does it handle edge cases?
2. Are there proper error messages?
3. Will I understand this code in 6 months?
4. Are dependencies properly typed?

Test Incrementally

Don't generate 500 lines of code at once. Instead:

• Generate small chunks (50-100 lines)
• Test each chunk before moving on
• Commit working code frequently
• Use Git to easily roll back if needed

When to Get Expert Help

Some bugs are too complex or time-consuming to debug yourself:

• Deployment issues you can't reproduce locally
• Performance problems that require profiling
• Security vulnerabilities in AI-generated code
• Complex architectural problems
• When you're on a deadline and need it fixed NOW

Conclusion: Master AI Debugging to Ship Faster

Debugging AI-generated code is a skill that separates hobbyists from professionals. With the right tools, workflows, and mindset, you can debug AI code just as effectively—if not more so—than traditional code.

Remember: The goal isn't to avoid bugs entirely. It's to develop a systematic process for finding and fixing them quickly so you can keep shipping.