Using AI to Code the Right Way: From Prompt to Review

Many people hit pitfalls when using AI for coding: generated code doesn't run, has security holes, or is unreadable and unmodifiable. The right approach is to treat AI as a pair-programming partner, not an outsourcer.

Approach 1: Describe the Problem, Don't Beg for Code
Bad: "Help me write a login function"
Good: "The existing system is PHP 7.4 + MySQL. I need a secure login endpoint for the existing users table. Requirements: SQL injection prevention, remember-me support, rate limiting on failed attempts. Please provide the implementation plan and key code."

Approach 2: Small Steps Iteratively
Have AI solve one small problem at a time and assemble gradually. Generating 500 lines of code at once has a far higher error rate than payoff.

Approach 3: Mandate Explanations
Follow up on every piece of AI-generated code: "Why is this logic written this way? How are edge cases handled?" If AI can't explain clearly, the code likely has issues.

Approach 4: Code Review
After writing, have AI act as a senior reviewer to find bugs: "Review the following code from three dimensions: security (injection/XSS), performance, readability."

Approach 5: Test-Driven
Have AI generate test cases first, then implement the feature. Use tests to verify the implementation is correct.

Core principle: The human is ultimately responsible for the code. AI handles speed and drafts; you handle correctness and maintainability.