Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity

• URL: http://arxiv.org/abs/2507.18638v2
• Date: Wed, 27 Aug 2025 21:28:06 GMT
• Title: Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity
• Authors: Rizal Khoirul Anam,
• Abstract summary: This paper investigates how the structure and clarity of user prompts impact the effectiveness and productivity of large language models (LLMs)<n>The results show that users who employ clear, structured, and context-aware prompts report higher task efficiency and better outcomes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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