PromptPilot: Improving Human-AI Collaboration Through LLM-Enhanced Prompt Engineering
Abstract
Effective prompt engineering is critical to realizing the promised productivity gains of large language models (LLMs) in knowledge-intensive tasks. Yet, many users struggle to craft prompts that yield high-quality outputs, limiting the practical benefits of LLMs. Existing approaches, such as prompt handbooks or automated optimization pipelines, either require substantial effort, expert knowledge, or lack interactive guidance. To address this gap, we design and evaluate PromptPilot, an interactive prompting assistant grounded in four empirically derived design objectives for LLM-enhanced prompt engineering. We conducted a randomized controlled experiment with 80 participants completing three realistic, work-related writing tasks. Participants supported by PromptPilot achieved significantly higher performance (median: 78.3 vs. 61.7; p = .045, d = 0.56), and reported enhanced efficiency, ease-of-use, and autonomy during interaction. These findings empirically validate the effectiveness of our proposed design objectives, establishing LLM-enhanced prompt engineering as a viable technique for improving human-AI collaboration.
Cite
@article{arxiv.2510.00555,
title = {PromptPilot: Improving Human-AI Collaboration Through LLM-Enhanced Prompt Engineering},
author = {Niklas Gutheil and Valentin Mayer and Leopold Müller and Jörg Rommelt and Niklas Kühl},
journal= {arXiv preprint arXiv:2510.00555},
year = {2025}
}
Comments
Preprint version. Accepted for presentation at the International Conference on Information Systems (ICIS 2025). Please cite the published version when available