The Right Way to Use AI for Coding (And How It Transformed My Workflow)

For a long time, I used AI for coding the wrong way — and chances are, you might be doing the same.

Most people treat AI like an automated code generator. They type in a vague prompt, copy the output, and paste it straight into their editor. No modifications, no understanding of what’s happening under the hood. The result? A mess of code that doesn’t work as expected.

But what if I told you that there’s a better way? A way to use AI not just to generate code but to build fully functional, high-quality projects?

After experimenting with different approaches, I found a structured method that boosted my coding efficiency, reduced errors, and even helped me build entire AI tools from scratch. In this post, I’ll walk you through the process and compare how different AI models performed in generating usable code.

Let’s dive in.

The Wrong Way People Use AI for Coding

I used to approach AI coding like this:

1. Write a generic prompt like “Generate a Python script for a to-do list app.”
2. Copy the AI-generated code and paste it into VS Code.