8 AI Coding Mistakes Junior Developers Make

Avoid these mistakes to get dramatically better results from your AI tools.

Mistake 1: Blindly accepting AI suggestions

AI code suggestions can contain bugs, security vulnerabilities, and anti-patterns. Always read and understand every line before accepting.

Mistake 2: Not testing AI-generated code

Write tests before and after using AI. AI code that works in isolation may break in your specific context.

Mistake 3: Using AI as a crutch instead of learning

AI should accelerate learning, not replace it. Understand WHY the code works, not just that it works.

Mistake 4: Pasting entire codebases without context

Give AI focused context: the specific function, the error, the expected behavior. Too much context produces worse results.

Mistake 5: Not using .cursorrules or similar configs

Configure your AI coding tool with project-specific rules. This prevents repeated corrections and inconsistent code style.

Mistake 6: Ignoring security implications

AI may generate code with SQL injection, XSS, or authentication vulnerabilities. Always review AI code through a security lens.

Mistake 7: Not iterating on solutions

First AI suggestion not working? Provide more context, try a different approach, or ask AI to explain its reasoning before trying again.

Mistake 8: Using AI for architecture decisions without understanding

AI can suggest architectures, but you need to understand the trade-offs. Use AI to learn about options, then make informed decisions.