After 30 years coding and 8 years in AI, I learned the key skill isn’t perfect prompts – it’s knowing when to keep tweaking vs starting fresh. Break prompts into pieces.
The story
After three decades of writing code and eight years building AI solutions, I’ve figured out the most important skill isn’t writing the perfect prompt – it’s knowing when to keep tweaking and when to throw it out and start fresh.
Iterate when you’re close. If the AI’s output has the right structure but needs adjustment, keep refining. Add constraints. Clarify instructions. This works great for creative work or consistent production results.
Start over when iteration stops working. If your prompt becomes a tangled mess of patches, or you’re only seeing tiny improvements, that’s your signal. Fresh start beats fighting a broken foundation.
Break problems into pieces. This is the highest-leverage technique I use. Treat prompts like code components – each handling one subtask. You can debug faster, reuse pieces, and update parts without rewriting everything.
Trust your gut. After decades in software, I can feel when a prompt needs a complete overhaul before the data proves it. That intuition matters.
The parallels to software development are obvious. We refactor messy code. We break monoliths into microservices. Prompts work the same way.
Bottom line: Be honest about whether tweaks are helping or just making things worse. With modular design and willingness to start fresh when needed, AI stops being a black box and becomes a reliable tool.
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