After 8 years with AI, I finally figured out why some people get great results while others just get frustrated: they’re trying to solve everything at once instead of breaking it down.
The story
I just had one of those lightbulb moments that feels obvious in hindsight but changes everything.
After 8 years of working with AI, I finally cracked why some people get incredible results while others just get frustrated: they’re trying to solve the whole problem at once.
Here’s what clicked for me yesterday. I was helping someone automate their content workflow, and they kept asking the AI to “write a complete marketing strategy.” The output? Generic garbage every single time.
So I broke it down. First prompt: identify the audience pain points. Second: brainstorm three angles. Third: develop one angle. Fourth: write the hook. Fifth: flesh out the body.
Same AI. Completely different results.
It’s like asking someone to “build a house” versus giving them specific tasks: pour the foundation, frame the walls, install the roof. The second approach works because each step has clear boundaries and success criteria.
The breakthrough isn’t about the AI’s capabilities. It’s about how we frame problems.
I’ve been coding for over 30 years, and this mirrors something I learned early: functions should do one thing well. The same principle applies to AI prompts. When you give AI a fuzzy, multi-layered task, it has to guess what you prioritize. When you break it into discrete chunks, it can focus.
The skill isn’t learning fancy prompt engineering tricks. It’s learning to dissect your own thinking.
What are you actually trying to accomplish? What’s the first atomic step? What comes next?
Most people skip this because it feels like extra work. But spending two minutes breaking do
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