优化提示词能显著提升大模型效率，助人更高效完成任务。
Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity
- 通过清晰结构化提示词提升大模型输出质量
- 243名用户调研显示，好提示使任务效率更高
- 适合希望高效使用AI的职场与学习人群
大规模语言模型（如ChatGPT、Gemini、DeepSeek）在教育、职业和创意领域的广泛应用，改变了人们的任务处理方式。本文基于243名来自不同学术与职业背景的受访者数据，分析了AI使用习惯、提示策略及用户满意度。结果表明，采用清晰、结构化且具上下文意识的提示词的用户，报告了更高的任务效率和更好的产出效果。研究强调了提示工程在最大化生成式AI价值中的关键作用，并为日常使用提供了实践启示。
原文摘要 · Abstract (English)
The widespread adoption of large language models (LLMs) such as ChatGPT, Gemini, and DeepSeek has significantly changed how people approach tasks in education, professional work, and creative domains. This paper investigates how the structure and clarity of user prompts impact the effectiveness and productivity of LLM outputs. Using data from 243 survey respondents across various academic and occupational backgrounds, we analyze AI usage habits, prompting strategies, and user satisfaction. The results show that users who employ clear, structured, and context-aware prompts report higher task efficiency and better outcomes. These findings emphasize the essential role of prompt engineering in maximizing the value of generative AI and provide practical implications for its everyday use.
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