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

==== Background & Research Question ====

Large language models (LLMs) like ChatGPT respond based on the prompt—the instruction you give them. Small wording changes can dramatically change the output, which makes “prompt engineering” tricky and often frustrating.

Existing work falls into two extremes:
* Manual prompting: humans handcraft prompts by trial-and-error.
* Automatic prompt optimization: algorithms search for better prompts, often without human oversight.

Both have problems. Manual prompting is time-consuming and not accessible to non-technical experts. Fully automatic methods often ignore domain nuance and user preferences (e.g., what counts as “good enough” or “readable” in a medical, legal, or educational setting).

Research question:
How can we combine human judgment and data-driven optimization so that people—especially non-technical domain experts—get guided help to create better prompts for their own tasks?