用智能导师帮程序员在写代码时学会写好提示词。
Prompt Coach: An Empirical Evaluation of an Agentic Tutor for Learning Prompt Engineering in Software Development
- 内置IDE的智能导师通过苏格拉底式提问引导开发者自纠提示词。
- 15名专业开发者单次60分钟训练后，提示词质量显著提升，尤其在常被忽略维度。
- 开发者普遍信任且愿长期使用，认为该工具有效提升写作能力。
提示词工程已成为软件开发中关键但未被充分教授的技能，传统学习方式难以应对其动态、互动和上下文依赖的特点。本文提出Prompt Coach（PC），一种嵌入开发环境的智能导师，通过苏格拉底式引导帮助开发者在实际编码中提升代码生成提示词的质量。PC从多个维度评估提示词，并基于开发者代码库与目标大模型行为，提出针对性问题促进自我修正。我们对15名专业开发者进行了初步实证研究，结合定量评分与定性反馈。参与者在单次60分钟训练后，提示词质量实现统计学显著提升，尤其在开发者常忽视的维度上进步明显。他们普遍表现出高度信任、高采纳意愿，并一致认为该工具有效提升了提示词编写能力。
原文摘要 · Abstract (English)
Prompt engineering has emerged as a critical yet undertaught skill for software developers, one that traditional learning approaches are ill-equipped to support given its evolving, interactive, and context-dependent nature. In this paper, we introduce Prompt Coach (PC), an agentic tutor that helps developers learn how to craft high-quality code-generation prompts through Socratic guidance embedded in-flow within their IDE. PC evaluates prompt quality across multiple dimensions and surfaces targeted questions to guide self-correction, grounded in the developer's codebase and the behavior of the target LLM. We present an early empirical study with 15 professional developers combining quantitative prompt quality scoring with qualitative perception measures. Participants showed statistically significant improvements after a single 60-minute session, with the largest gains across dimensions commonly overlooked by developers. They also reported strong trust, high adoption readiness, and unanimous agreement that PC improved their prompt-writing skills.
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