Editing Openai/6910f96f-f278-8006-9996-5cf14ff7a27f (section)

==== Why It Matters / Implications ====

In practice today, many people—from doctors and lawyers to teachers and analysts—are expected to “just know” how to write good prompts, often without technical training. At the same time, fully automatic prompt optimizers may ignore important human preferences like clarity, domain nuance, or acceptable trade-offs between accuracy and simplicity.

This paper’s contribution is to:
* Propose a concrete, structured workflow where human judgment and AI optimization are combined, rather than treated as separate worlds.
* Provide a prototype web tool that could help non-technical domain experts gradually arrive at effective prompts for their own classification tasks using their own data.
* Offer a research framework to study: - Which prompt properties (examples, explanations, readability, metrics) actually guide human choices, - How prompts evolve over multiple optimization steps, and - How much human involvement is really needed before automation can take over.

In short, iPrOp moves prompt engineering away from pure guesswork toward a more systematic, transparent, and collaborative process between humans and LLMs.