By skyforbes 
I’m going to be blunt: most AI-generated code looks like a talented intern who learned everything from Stack Overflow and bad habits. It can be clever. It can be fast. But it rarely ships without supervision.
That stopped annoying me the day I stopped treating these models like glorified search bars and started treating them like junior engineers who needed a process, a persona, and a checklist. This one prompt made Cursor, Claude, and ChatGPT produce code that I could actually read, test, and sometimes drop into a repo with minimal edits.
Below I tell you the exact prompt I use, why it works (with citations), and how to adapt it to different workflows. This is practical, battle-tested (my kind of testing: lots of late nights coffee and blue screen filter), and opinionated. You’re welcome.
Why most prompts fail (and why that’s your fault)
Generative models are trained to continue text, not to design systems. Ask for “a function that does X” and you’ll often get a function that looks correct but misses validation, side effects, or edge cases. Models are excellent at producing plausible code; they are not automatically excellent at producing robust software. That’s because:
- They optimize for completion given the next-token distribution, not for engineering tradeoffs. (OpenAI’s own guidance shows that structure and specificity improve outcomes.)