Prompt Engineering and LLM Effectiveness in Enhancing Human Productivity (arXiv:2507.18638)

Forum topic · ✨步子哥 · 2025-11-30

Summary

A 2025 paper by Rizal Khoirul Anam (arXiv:2507.18638) examines how the structure and clarity of user prompts affect the effectiveness of large language models such as ChatGPT, Gemini, and DeepSeek in boosting human productivity. Based on survey data from 243 respondents across academic and occupational backgrounds, the study analyzes AI usage habits, prompting strategies, and user satisfaction. Reported results include 42% higher task efficiency with clear, structured prompts, a 35% reduction in iterations with context-aware prompts, 38% higher satisfaction among users with prompt engineering skills, and 28% time saved on revisions. Key effective strategies include clarity and specificity, context inclusion, structured formatting, examples, step-by-step instructions, and role assignment. The paper concludes that prompt engineering is a critical competency for maximizing generative AI value, recommending that educators incorporate it into curricula and organizations provide AI interaction training.

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

Author: Rizal Khoirul Anam | Published: August 26, 2025 | arXiv: arXiv:2507.18638

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, the study analyzes 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.

Research Methodology

Survey of 243 respondents across academic and occupational backgrounds
Analysis of AI usage habits, prompting strategies, and user satisfaction
Comparative evaluation of prompt effectiveness across different tasks
Statistical correlation between prompt quality and task efficiency

Key Findings

| Metric | Result | |---|---| | Higher task efficiency with clear, structured prompts | 42% | | Reduction in required iterations with context-aware prompts | 35% | | Higher satisfaction among users with prompt engineering skills | 38% | | Time saved on revisions with effective prompts | 28% |

Specificity in prompts correlates with output relevance and accuracy
Structured prompts yield more consistent and reliable results
Context-aware prompts significantly reduce the need for clarification

Effective Prompt Strategies

1. Clarity and Specificity — use precise language and detailed instructions 2. Context Inclusion — provide relevant background information 3. Structure — organize prompts with clear sections or formatting 4. Examples — include examples to guide the model's response style 5. Step-by-Step Instructions — break complex tasks into smaller steps 6. Role Assignment — specify the persona or perspective for the response

Practical Implications

Educational institutions should incorporate prompt engineering into curricula
Organizations should provide training on effective AI interaction
Development of prompt engineering tools and frameworks can enhance productivity
Standardization of best practices for prompt design across industries
Recognition of prompt engineering as a valuable professional skill

Conclusion

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 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.