It’s becoming more and more common to use AI to help you understand what AI is doing. Task decomposition has been around a long time with asking AI to break things into steps to implement. This is kind of a new technique I had not seen: prompt decomposition. The idea is to use AI to help generate a good prompt that will get you want you want.

This could be used on cheaper local models you run for free to generate the prompts you use on expensive models.

The query goes something like this:

I want to create a high-quality prompt for this task:

[TASK]

Before writing the prompt, identify the 5–7 high-leverage prompt dimensions for this task — the core variables, constraints, context, output requirements, or stylistic choices that will most determine the quality of the result.

For each dimension, briefly explain:

1. Why it matters

2. What tradeoff or decision it controls

3. How it should influence the final prompt

Then turn those dimensions into a polished, copy-ready prompt.

The final prompt should be clear, specific, and structured. It should include the necessary context, role, task instructions, constraints, output format, quality criteria, and guidance for handling ambiguity.