This content originally appeared on DEV Community and was authored by Арсений Перель
I’ve been experimenting with a simple idea:
Maybe many unstable LLM outputs are caused not by the model itself, but by badly structured prompts.
So I built a web tool that refactors messy prompts into structured prompt specifications.
Instead of asking the model to “improve” a prompt once, the system runs an optimization loop:
- Proposer restructures the prompt
- Critic evaluates clarity, structure, and task definition
- Verifier checks consistency
- Arbiter decides whether another iteration is needed
The output is a structured prompt spec with:
- sections
- explicit requirements
- output constraints
- improved clarity
The full optimization usually takes around 30–40 seconds.
Demo:
https://how-to-grab-me.vercel.app/
What I’m trying to validate now is simple:
Should prompt refactoring become a standard preprocessing layer for LLM workflows?
This content originally appeared on DEV Community and was authored by Арсений Перель
Арсений Перель | Sciencx (2026-03-13T15:30:25+00:00) I built a prompt refactoring engine using a Proposer–Critic–Verifier pipeline. Retrieved from https://www.scien.cx/2026/03/13/i-built-a-prompt-refactoring-engine-using-a-proposer-critic-verifier-pipeline-3/
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