30+ years of coding taught me this about AI prompts: it’s not about finding the “perfect” prompt, it’s knowing when to iterate vs start over completely.
The story
I’ve been coding for over three decades, and here’s something I just figured out about working with AI that nobody really talks about:
Most people think prompt engineering is about finding the “perfect” prompt. It’s not.
It’s about knowing when you’re close versus when you’re fundamentally off track.
Here’s what I mean: When I write a prompt and the output is almost there – maybe the tone’s wrong or it’s missing a detail – that’s when I iterate. Small tweaks. Add specificity. Clarify the format. Adjust the role I’ve assigned the AI. Each cycle gets me closer.
But when the AI consistently misunderstands what I’m asking? That’s different. No amount of tweaking fixes a fundamentally misaligned prompt. That’s when I scrap it and start over.
The trick is recognizing the difference.
I call it “vibe coding” – you develop an intuition for when you’re in refinement mode versus when you’re forcing a bad foundation to work. It’s a meta-skill that emerges with practice.
Context windows matter too. If your problem is too complex to fit, or if the AI keeps generating nonsense despite your refinements, the structure itself is probably flawed. Start fresh with a modular approach.
Here’s the practical bit: Most prompts need iteration, not replacement. Break complex tasks into chunks. Be specific – move from general to precise. Assign roles. Use structured formats.
And track your versions. In my eight years working with AI solutions, I’ve learned that collaborative refinement beats solo guesswork every time.
The future isn’t about perfect prompts on the first try. It’s about knowin
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