动态提示中间件让用户更灵活地优化AI解释，提升对生成内容的控制力。
Dynamic Prompt Middleware: Contextual Prompt Refinement Controls for Comprehension Tasks
- 根据用户输入和需求动态生成提示优化选项，实时调整解释方向。
- 用户研究显示，动态方案比固定模板更易用，能降低表达难度。
- 适合希望精准控制AI解释过程的研究者与非技术用户使用。
为提升用户在理解任务（如解释表格公式、Python代码、文本段落）中生成式AI的提示效果，现有提示中间件仍难以满足用户对输出控制的需求。我们通过一项包含38名参与者的前期调研，发现标准化支持虽可预测但缺乏灵活性，而自适应支持虽个性化却不可控。为此，我们实现两种中间件方法：动态提示精炼控制（Dynamic PRC）与静态提示精炼控制（Static PRC）。前者基于用户输入和需求动态生成上下文相关的优化界面元素；后者提供一组通用预设优化项。在16名用户的对照实验中，动态方案显著提升了用户对生成结果的控制感，降低了提供上下文的门槛，并促进任务反思与探索，但理解不同控制对输出的影响仍具挑战。基于用户反馈，我们提出未来系统设计应增强用户对控制效果的感知能力。结果表明，动态提示中间件可有效改善生成式AI工作流体验，赋予用户更高控制权并引导获得更优响应。
原文摘要 · Abstract (English)
Effective prompting of generative AI is challenging for many users, particularly in expressing context for comprehension tasks such as explaining spreadsheet formulas, Python code, and text passages. Prompt middleware aims to address this barrier by assisting in prompt construction, but barriers remain for users in expressing adequate control so that they can receive AI-responses that match their preferences. We conduct a formative survey (n=38) investigating user needs for control over AI-generated explanations in comprehension tasks, which uncovers a trade-off between standardized but predictable support for prompting, and adaptive but unpredictable support tailored to the user and task. To explore this trade-off, we implement two prompt middleware approaches: Dynamic Prompt Refinement Control (Dynamic PRC) and Static Prompt Refinement Control (Static PRC). The Dynamic PRC approach generates context-specific UI elements that provide prompt refinements based on the user's prompt and user needs from the AI, while the Static PRC approach offers a preset list of generally applicable refinements. We evaluate these two approaches with a controlled user study (n=16) to assess the impact of these approaches on user control of AI responses for crafting better explanations. Results show a preference for the Dynamic PRC approach as it afforded more control, lowered barriers to providing context, and encouraged exploration and reflection of the tasks, but that reasoning about the effects of different generated controls on the final output remains challenging. Drawing on participant feedback, we discuss design implications for future Dynamic PRC systems that enhance user control of AI responses. Our findings suggest that dynamic prompt middleware can improve the user experience of generative AI workflows by affording greater control and guide users to a better AI response.
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