PromptPilot用AI助手帮人高效写提示词，提升人机协作效果。
PromptPilot: Improving Human-AI Collaboration Through LLM-Enhanced Prompt Engineering
- 基于四个实证设计目标，打造交互式提示词助手。
- 用户使用后任务表现中位数从61.7升至78.3，显著更优。
- 适合需要高效使用大模型的科研、写作等场景。
有效的提示词工程对实现大语言模型在知识密集型任务中的生产率提升至关重要。然而，许多用户难以编写出高质量输出的提示词，限制了大模型的实际效益。现有方法如提示词手册或自动化优化流程，或需大量投入、依赖专家知识，或缺乏交互引导。为此，我们设计并评估了PromptPilot——一个基于四个实证得出的设计目标构建的交互式提示词助手。通过随机对照实验，80名参与者完成三项真实工作相关的写作任务。使用PromptPilot的参与者表现显著更优（中位数：78.3 vs. 61.7；p = .045，d = 0.56），且报告效率更高、操作更简便、自主性更强。这些发现实证验证了所提设计目标的有效性，确立了大模型增强的提示词工程作为提升人机协作的可行方案。
原文摘要 · Abstract (English)
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.
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