How to Get Good Code From AI

It’s Not Just Prompts

After over a year of using AI coding tools, I’ve learned something important.

Everyone says “write better prompts.” But that’s only half the story. Before prompts, your project structure needs to be solid.

AI generates code based on context. If your project is a mess, even great prompts produce messy code. In a clean project, even casual instructions yield clean code.

Practical Tips From Real Use

1. Keep files small.

Stuff 500 lines into one file and AI loses context. Break files into small, focused units. The AI grasps each file’s purpose and generates appropriate code.

2. Invest in naming.

Clear variable and function names help AI understand intent. userProfileData beats data. calculateMonthlyRevenue beats process. Better names, dramatically better results.

3. Work in stages.

“Build this entire app” produces garbage. “Design the DB schema first” → “Create APIs based on this schema” → “Write tests for these APIs” — breaking it into steps dramatically improves quality.

4. Show examples.

If existing code follows a pattern, “create it following the same pattern as this file” is the most effective instruction. AI excels at pattern replication.

5. Specify edge cases.

“Handle errors when this function returns null.” “Handle negative input values.” Explicitly mentioning edge cases produces significantly more robust code.

Development Skills Matter More Than Ever

Ironically, using AI coding tools well requires strong development skills.

You need to judge whether AI-generated code is correct. Architecture design, code review, and test writing have become more important in the AI era, not less.

“AI handles everything, no need to learn coding” is floating around. Reality is the opposite. Using AI effectively requires a solid development foundation. I wrote about developer laziness — having AI doesn’t mean you stop learning.

AI is a tool. A skilled carpenter with good tools creates masterpieces. A beginner with good tools does not.