发表机构
School of Computer Science and Technology, Guangdong University of Technology; Department of Computer Science, Hong Kong Baptist University（广东工业大学计算机科学与技术学院; 香港浸会大学计算机科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究从用户视角开展大型语言模型提示工程调查，提出提示有效性评估策略、应用工作流并维护开源项目，为用户制定适配场景的有效提示提供可操作指南。
AI 中文摘要
由大型语言模型（LLMs）驱动的AI工具如ChatGPT和DeepSeek，用户只需输入请求即可获得即时有效的内容响应，例如“规划为期三天的维也纳之旅”“求解附带的数学题”“起草询问评审进度的邮件”等，这些请求也被称为LLM提示。构建清晰且结构良好的提示能让LLM给出更恰当的反馈，有效弥合人类与LLM的交互。尽管提示看似对非专业用户易于上手，但精准构建有效提示是一个高度系统化且需要技巧的过程，即便对经验丰富的用户也存在潜在挑战。本调查从用户中心视角探究提示的原则、分类与组织方式，与现有主要聚焦LLM技术原则和应用场景的调查不同，本文为各类现实任务提供了可操作的有效LLM提示制定指南，具体贡献包括：1）开发直观的提示有效性评估策略；2）展示代表性应用的提示工作流；3）维护动态更新的开源项目以确保核心要点与时俱进。这些措施降低了用户正确理解并构建适配不断发展的应用场景的提示的门槛，本工作将作为动态GitHub项目维护，详见此处。
英文摘要
AI tools like ChatGPT and DeepSeek, powered by Large Language Models (LLMs), allow users to obtain instant and effective content responses simply by typing requests, such as ``plan a three-day Vienna trip'', ``solve the attached mathematical problem'', ``draft an email to inquire review progress'', etc., which are also known as LLM prompts. Crafting clear and well-structured prompts leads to more appropriate LLM feedback, which effectively bridges human-LLM interaction. Although prompting appears accessible to non-expert users, precisely organizing effective prompts is a highly systematic and skillful process, presenting potential challenges even for experienced users. This survey explores the principles, taxonomy, and organization of prompts from a user-centered perspective. Differing from the existing surveys that primarily focus on technical principles and application scenarios of LLMs, this paper provides actionable guidelines for formulating effective LLM prompts across diverse real-world tasks and specifically contributes by: 1) developing an intuitive evaluation strategy for prompt efficacy, 2) providing prompting workflow demonstrations on representative applications, and 3) maintaining a dynamically updated open-source project to ensure the core takeaways remain up-to-date. These measures lower the threshold for users to correctly understand and craft prompts that align with evolving application scenarios. This work will be maintained as a living GitHub project \href{https://github.com/Yunfan-Zhang/TAI_Guideline-Table}{\textcolor{blue}{here}}.
CommentsAccepted to TAI
