Working with LLMs: Avoiding Common Pitfalls and Maintaining Code Quality

AI coding assistants like Claude, Cursor, and GitHub Copilot have revolutionized how we write code. But with great power comes great responsibility—and the potential for some serious pitfalls. The key is learning how to leverage these tools effectively while maintaining your coding standards and avoiding the traps that can lead to technical debt.

1. Document LLM Mistakes for Future Reference

One of the most important practices when working with LLMs is to document their recurring mistakes. This creates a feedback loop that improves your future interactions and helps you catch issues before they become problems.

Create a Mistake Log

Keep a dedicated file or section in your project documentation to track common LLM mistakes. This becomes your reference guide for future interactions.

# LLM Mistake Log

## Common Issues and Solutions

### 1. Over-Engineering Simple Functions
**Problem**: LLM suggests complex solutions for simple problems
**Example**: Using a full state management system for a simple form
**Solution**: Always ask "Is this the simplest possible solution?"

### 2. Ignoring Early Returns
**Problem**: LLM creates deeply nested if-else statements
**Example**: 
```javascript
// ❌ LLM suggestion
function validateUser(user) {
  if (user) {
    if (user.email) {
      if (user.email.includes('@')) {
        return true;
      } else {
        return false;
      }
    } else {
      return false;
    }
  } else {
    return false;
  }
}
```
**Solution**: Request early returns explicitly
**Better Version**:
```javascript
function validateUser(user) {
  if (!user) return false;
  if (!user.email) return false;
  return user.email.includes('@');
}
```

### 3. Generic Variable Names
**Problem**: LLM uses vague names like 'data', 'result', 'value'
**Solution**: Always request descriptive, specific names

### 4. Missing Error Handling
**Problem**: LLM focuses on happy path only
**Solution**: Explicitly ask for error handling and edge cases

### 5. Over-Documentation
**Problem**: LLM adds comments for obvious code
**Solution**: Request "comment the why, not the what"

## How to Use This Log
- Reference before starting new LLM sessions
- Add new patterns as you discover them
- Share with team members for consistency
- Update your .cursorrules or system prompts based on findings

Update Your Configuration Files

Use your mistake log to continuously improve your LLM configuration:

# Common LLM Mistakes to Avoid
- Don't over-engineer simple solutions
- Always use early returns instead of deep nesting
- Use descriptive variable names (avoid 'data', 'result', 'value')
- Include error handling and edge cases
- Comment the "why", not the "what"
- Keep functions small and focused
- Prefer simple, readable code over clever solutions

2. Don't Let AI Compliments Go to Your Head

Why This Matters

When an LLM says "This is excellent code!" or "Great approach!", it can create a false sense of security. You might be less likely to question the code quality or push for improvements. Remember: the LLM doesn't understand your specific coding standards or project requirements.

The LLM Compliment Bingo

LLMs have a predictable pattern of overly enthusiastic responses. Here are some classic phrases that should trigger your "stay objective" alarm:

Maintaining Strict Standards

Keep your coding principles front and center, regardless of what the LLM says:

LLM: "This is excellent code! Very clean and well-structured."

You: "Thanks, but let's review it against our standards:
- Is this the simplest possible solution?
- Are we using early returns?
- Are the variable names descriptive enough?
- Could this function be broken down further?"

LLM: "You're right, let me improve it..."

The Feedback Loop

Use compliments as opportunities to reinforce your standards:

• • Don't accept praise at face value - always verify against your principles
• • Use compliments as teaching moments - explain what you're looking for
• • Stay objective - focus on code quality, not ego
• • Maintain consistency - apply the same standards regardless of AI feedback

3. Common LLM Development Pitfalls

Pitfall 1: Blind Trust

Problem: Assuming the LLM's code is correct without review

Solution: Always review and understand the code before implementing. Ask questions, request explanations, and verify it follows your principles.

Pitfall 2: Over-Reliance

Problem: Using the LLM for every decision, losing your own judgment

Solution: Use the LLM as a tool, not a replacement for your expertise. Make your own architectural decisions and use the LLM for implementation help.

Pitfall 3: Inconsistent Standards

Problem: Letting the LLM's style override your team's coding standards

Solution: Always enforce your coding standards. If the LLM suggests something that doesn't match your principles, ask for alternatives or modify the code.

Pitfall 4: Copy-Paste Development

Problem: Using LLM-generated code without understanding it

Solution: Always understand the code you're implementing. Ask the LLM to explain complex parts, and make sure you can maintain and modify the code.

4. Effective LLM Interaction Strategies

The Review Process

Establish a consistent review process for LLM-generated code:

Asking the Right Questions

Use specific questions to guide the LLM toward your standards:

5. Building a Sustainable LLM Workflow

Daily Practices

Integrate these practices into your daily development workflow:

Continuous Improvement

Treat your LLM interaction as a skill that needs development:

• • Track your success rate - How often does the LLM generate code that meets your standards?
• • Refine your prompts - Based on what works and what doesn't
• • Share learnings with your team - Create shared mistake logs and best practices
• • Stay updated - LLM capabilities change, so should your approach

6. Team Integration

Shared Standards

When working with a team, ensure everyone follows the same LLM practices:

Code Review Checklist

Add these items to your code review process for LLM-generated code:

• • Does the code follow our established principles?
• • Is this the simplest possible solution?
• • Are variable and function names descriptive?
• • Does the code handle edge cases and errors?
• • Is the code maintainable and readable?
• • Does the developer understand the code they're implementing?