Breaking complex AI tasks into smaller chunks gets way better results than asking it to solve everything at once. Here’s my 30-year coding process for decomposition prompting.
The story
After three decades writing code and eight years deep in AI, I’ve cracked something important: the best way to get killer results from AI isn’t asking it to solve everything at once.
It’s chunking.
When I need an AI coding assistant to build something complex, I break the problem into smaller pieces first. I guide it through each part separately, then pull everything together. This approach – called decomposition prompting – has become my secret weapon.
Here’s my process:
I start by having the AI map out all the sub-tasks. Instead of “build me an app,” I ask “list the key modules this needs.” Suddenly, the chaos becomes manageable.
Then I tackle each piece individually. “Write the login module.” “Handle data validation.” Each prompt stays focused, which means fewer errors and cleaner code.
Finally, I synthesize. The AI [or I] weave those pieces into something that actually works.
The skills that make this work:
Prompt engineering matters more than people think. I’ve learned to be specific – asking for numbered lists or tables when I need structure, using precise language that leaves no room for confusion.
Modularization is second nature after decades of coding. I design tasks so each piece can be built, tested, and reused independently. AI loves this.
Managing multiple agents for complex workflows keeps each AI focused on its lane while contributing to the bigger picture.
Iterative refinement catches problems early. I test outputs, adjust my prompts, and loop until it’s right.
The traps I avoid: vague definitions derail everything, so I set clear boundaries for
Recent Comments