Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity
It addresses the problem of optimizing LLM usage for improved productivity in education, professional work, and creative domains, though it is incremental as it builds on existing prompt engineering concepts.
This paper investigated how prompt structure and clarity affect the effectiveness of large language models (LLMs) in enhancing human productivity, finding that users employing clear, structured, and context-aware prompts reported higher task efficiency and better outcomes based on data from 243 survey respondents.
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.