🤖 AI Summary
This study investigates how prompt structure and clarity affect large language model (LLM) output quality and user productivity. Drawing on empirical data from 243 users across education, professional, and creative domains, the research integrates questionnaire surveys, behavioral log analysis, and satisfaction assessments. It provides the first systematic validation that structured, context-sensitive, and semantically explicit prompts significantly improve task completion efficiency (+37.2%) and output adherence rate (+41.5%). Methodologically, the study introduces the first multi-context, user-driven evaluation framework for prompt engineering efficacy. Its key contributions include three generalizable, transferable principles for high-efficiency prompt design. Results demonstrate that prompt engineering transcends mere technical fine-tuning—it functions as a critical leverage point for unlocking LLMs’ practical productivity in real-world applications.
📝 Abstract
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.