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AI Dos and Don'ts

Essential guidelines for mid-level programmers using AI tools responsibly and effectively. Learn to leverage AI while maintaining code quality and professional integrity.

🎯 Core Principles for Mid-Level Developers

Your Advantage:

• You can evaluate AI suggestions critically
• You understand code architecture and patterns
• You know when something "smells" wrong
• You can debug and maintain complex code

Your Responsibility:

• Guide junior developers in AI usage
• Maintain code quality standards
• Balance AI assistance with skill development
• Ensure team best practices

What TO DO

Learning & Development

Use AI as a Learning Tool

Ask AI to explain complex concepts, algorithms, or code patterns you don't understand

Example: Instead of copying code blindly, ask: "Can you explain how this sorting algorithm works step by step?"

Review and Understand AI Code

Always read through AI-generated code and make sure you understand what it does

Example: Test the code, add comments, and modify it to fit your specific needs

Use AI for Boilerplate and Repetitive Tasks

Let AI handle routine code generation so you can focus on architecture and logic

Example: Generate CRUD operations, API endpoints, or configuration files

Iterate and Refine Prompts

If the first response isn't perfect, refine your prompt with more context

Example: Start broad, then add specific requirements, constraints, and examples

Code Quality & Best Practices

Apply Your Experience

Use your mid-level expertise to evaluate and improve AI suggestions

Example: Check for security issues, performance problems, and maintainability concerns

Combine AI with Testing

Write tests for AI-generated code to ensure it works correctly

Example: Generate unit tests alongside the main code, and run them to verify functionality

Use AI for Code Reviews

Ask AI to review your code for potential issues or improvements

Example: Paste your code and ask: "What potential issues do you see in this code?"

Document AI-Assisted Work

Be transparent about AI usage in your commits and documentation

Example: Add comments like "// Generated with AI assistance, reviewed and tested"

What NOT TO DO

Learning & Skill Development

Don't Skip Learning Fundamentals

AI can't replace understanding core programming concepts and principles

⚠️ Warning: You'll struggle with debugging and architecture if you rely too heavily on AI

Don't Copy-Paste Without Understanding

Blindly using AI code without comprehension leads to technical debt

⚠️ Warning: You won't be able to maintain or debug code you don't understand

Don't Let AI Make All Design Decisions

Architecture and design patterns require human judgment and experience

⚠️ Warning: AI might suggest technically correct but inappropriate solutions

Don't Become Overly Dependent

Maintain your ability to code without AI assistance

⚠️ Warning: You might face situations where AI tools aren't available

Security & Professional Risks

Don't Share Sensitive Information

Never paste proprietary code, API keys, or confidential data into AI tools

⚠️ Warning: This could violate NDAs and expose your company to security risks

Don't Trust AI for Security-Critical Code

Always have security experts review authentication, encryption, and access control code

⚠️ Warning: AI might miss subtle security vulnerabilities

Don't Assume AI Code is Bug-Free

AI-generated code can contain logical errors, edge case issues, or performance problems

⚠️ Warning: Thorough testing is essential for all AI-assisted code

Don't Ignore Licensing and Copyright

Be aware that AI might generate code similar to copyrighted material

⚠️ Warning: Research the legal implications of AI-generated code in your jurisdiction

Ethical Considerations

Transparency

Be open about AI usage with your team and stakeholders

• Disclose AI assistance in code reviews
• Document which parts were AI-generated
• Be honest about your capabilities vs AI capabilities

Responsibility

Take ownership of all code that goes into production

• Test and validate all AI-generated code
• Ensure you can maintain and debug the code
• Take responsibility for any issues that arise

Continuous Learning

Use AI to enhance, not replace, your learning journey

• Ask AI to explain concepts you don't understand
• Practice coding without AI regularly
• Focus on understanding patterns and principles

Team Collaboration

Consider how AI usage affects your team dynamics

• Share AI techniques that work well
• Help teammates who are less familiar with AI
• Establish team guidelines for AI usage

Common Mistakes to Avoid

Over-relying on AI for Problem Solving

Consequence: Weakened analytical and debugging skills

Solution: Set aside time for coding without AI assistance

Not Validating AI Suggestions

Consequence: Introducing bugs or inefficient code

Solution: Always test and review AI-generated code thoroughly

Using AI for Unfamiliar Technologies

Consequence: Implementing solutions you can't maintain

Solution: Learn the basics of new technologies before using AI

Ignoring Code Style and Standards

Consequence: Inconsistent codebase and harder maintenance

Solution: Configure AI to follow your team's coding standards

Not Considering Performance Implications

Consequence: Slow or resource-intensive applications

Solution: Profile and optimize AI-generated code

📋 Quick Reference: AI Best Practices

Before Using AI:

• Define clear requirements
• Consider security implications
• Check company policies
• Plan for testing and review

While Using AI:

• Provide clear, specific prompts
• Iterate and refine requests
• Ask for explanations
• Request multiple approaches

After AI Generation:

• Review and understand code
• Test thoroughly
• Refactor if needed
• Document AI assistance

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