PromptPilot: Improving Human-AI Collaboration Through LLM-Enhanced Prompt Engineering
This addresses the challenge for users of LLMs in knowledge-intensive tasks by providing an interactive tool to enhance prompt engineering, though it is incremental as it builds on existing prompt engineering techniques.
The paper tackled the problem of users struggling to craft effective prompts for LLMs, which limits productivity gains, and found that PromptPilot, an interactive prompting assistant, significantly improved performance in writing tasks with a median score of 78.3 compared to 61.7.
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.