一句话：提出 Prompt Triangle 模型，将 AI 编码提示视为混合需求与解决方案信息的演化制品，并给出实证议程。
随着 AI 编码助手普及，软件开发实践从编写代码转向编写提示，但提示尚未被充分理解为需求制品。作者主张提示是轻量、演化中的需求制品，会有意混入解决方案空间的信息。为此提出 Prompt Triangle 概念模型，包含功能与质量（需求）、通用解决方案（策略与技术选择）和具体解决方案（实现级约束）三个组成部分。基于此提出若干假设：提示会在这三类组件间演化，受用户因素影响，并随时间在功能上趋于稳定；三类组件的完整性与代码质量相关。论文并未报告实证结果，而是规划了语料库构建、纵向研究、受控实验以及需求感知提示指南开发等实证议程。
AI coding assistants shift software practice from writing code to crafting prompts. We argue that prompts should be treated as lightweight, evolving requirement artifacts that intentionally mix in information from the solution space. We propose a simple conceptual model – the Prompt Triangle – with three components: Functionality and Quality (the requirement), General Solutions (solution strategy and technology choices) and Specific Solutions (implementation-level constraints). We hypothesize that prompts evolve across these components, are shaped by user factors, stabilize on functionality over time, and that completeness across all three components correlates with code quality. We outline an empirical agenda to test these hypotheses through corpus creation, longitudinal studies, controlled experiments and the development of requirements-aware prompting guidelines.