After 30 years coding and 8 in AI, I’ve found one skill that changes everything: breaking problems into clean chunks before asking AI for help. Most people skip this step and wonder why their results are messy.
The story
I just spent the morning breaking down a gnarly coding problem, and it hit me again how much this one skill – decomposition – changes everything when working with AI.
After three decades writing code and eight years deep in AI development, I’ve learned that the real magic isn’t in the tools. It’s in how you slice up the problem before you ever ask AI for help.
Here’s what actually works:
First, I look at what I’m really trying to accomplish. Not the surface request, but the actual objective. That clarity reveals natural breaking points.
Then I identify the core components – what depends on what, where the patterns are, what can stand alone. Each chunk needs clean boundaries. No fuzzy overlap.
I structure these pieces hierarchically. Sometimes top-down from the big picture, sometimes bottom-up from simple building blocks. Either way, the relationships become obvious.
The prompt for each chunk matters more than most people think. I write it like I’m briefing a sharp junior developer: context, clear instructions, measurable outcome. Consistent language throughout.
Then I test and refine. First pass is never perfect. I evaluate each output independently and adjust the boundaries as needed.
The skills that make this work: prompt engineering, modularization, and staying flexible enough to let AI handle the routine stuff while I focus on architecture and design.
Advanced techniques like plan-and-solve prompting or recursive decomposition for huge problems make a real difference too.
The payoff? Lower cognitive load, fewer errors, transparent results, and the ability to s
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