After 8 years with AI, I’ve learned the secret isn’t perfect prompts – it’s iteration. Treat each response as feedback, refine your approach, and know when to start over.
The story
I’ve been working with AI for eight years now, and here’s what I’ve learned about prompting: iteration is almost always the move.
When I get a response that’s close but not quite right – maybe it missed a detail or drifted in tone – I don’t trash it. I tweak the prompt and try again. After three decades in software, I treat this like debugging code. Refine the language. Add context. Break complex requests into smaller chunks. Run it again.
The key is treating each output as feedback. If the AI misunderstands something, I get more specific. If it’s too vague, I add constraints. If it’s too wordy, I tell it to cut the fluff. Most problems can be solved by making your instructions clearer or restructuring how you ask.
But sometimes – rarely – you need to start over. If I’ve tweaked a prompt five times and it’s still missing the mark, that’s a signal. The foundation is probably wrong. Maybe the prompt is too vague, or I’m asking the AI to do something it’s not built for. When that happens, I step back and rethink the entire approach.
The difference between people who get great results from AI and those who don’t usually comes down to this: willingness to iterate, paired with knowing when to pivot. It’s not magic. It’s a skill you build through practice and feedback loops.
If you want AI to work for you, stop expecting perfection on the first try. Expect iteration. And get comfortable with it.
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