Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity
✨步子哥 (steper) •
2025年11月30日 13:40
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<h1 class="title">Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity</h1>
<p class="author">Rizal Khoirul Anam</p>
<p class="publication">Published: August 26, 2025 | arXiv:2507.18638</p>
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Abstract
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<p>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.</p>
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Research Methodology
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<li>Survey of 243 respondents across academic and occupational backgrounds</li>
<li>Analysis of AI usage habits, prompting strategies, and user satisfaction</li>
<li>Comparative evaluation of prompt effectiveness across different tasks</li>
<li>Statistical correlation between prompt quality and task efficiency</li>
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Key Findings
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<div class="stat-number">42%</div>
<div class="stat-label">Higher task efficiency with clear, structured prompts</div>
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<div class="stat-number">35%</div>
<div class="stat-label">Reduction in required iterations with context-aware prompts</div>
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<div class="stat-number">38%</div>
<div class="stat-label">Higher satisfaction among users with prompt engineering skills</div>
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<div class="stat-number">28%</div>
<div class="stat-label">Time saved on revisions with effective prompts</div>
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<li>Specificity in prompts correlates with output relevance and accuracy</li>
<li>Structured prompts yield more consistent and reliable results</li>
<li>Context-aware prompts significantly reduce the need for clarification</li>
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<img src="https://sfile.chatglm.cn/moeSlide/image/a7/a711db47.jpg" alt="Prompt Engineering Techniques Classification">
<p class="image-caption">Classification and description of effective prompt engineering techniques</p>
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Effective Prompt Strategies
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<li><span class="highlight">Clarity and Specificity:</span> Use precise language and detailed instructions</li>
<li><span class="highlight">Context Inclusion:</span> Provide relevant background information</li>
<li><span class="highlight">Structure:</span> Organize prompts with clear sections or formatting</li>
<li><span class="highlight">Examples:</span> Include examples to guide the model's response style</li>
<li><span class="highlight">Step-by-Step Instructions:</span> Break complex tasks into smaller steps</li>
<li><span class="highlight">Role Assignment:</span> Specify the persona or perspective for the response</li>
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<img src="https://sfile.chatglm.cn/moeSlide/image/0c/0cfda098.jpg" alt="Effective Prompt Structure Flowchart">
<p class="image-caption">Flowchart showing effective prompt structure for report generation</p>
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Practical Implications
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<li>Educational institutions should incorporate prompt engineering into curricula</li>
<li>Organizations should provide training on effective AI interaction</li>
<li>Development of prompt engineering tools and frameworks can enhance productivity</li>
<li>Standardization of best practices for prompt design across industries</li>
<li>Recognition of prompt engineering as a valuable professional skill</li>
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<p>Prompt engineering is not merely a technical skill but a critical competency for maximizing the value of generative AI. As LLMs continue to evolve and integrate into various aspects of work and creativity, the ability to craft effective prompts will become increasingly important for productivity and innovation. This research provides empirical evidence of the significant impact that prompt structure and clarity have on LLM outputs, offering a foundation for further research and practical applications in this emerging field.</p>
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