Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 5 of its 13 references that are in the map. First posted on arXiv 10 May 2025.
Builds on
5 of 13 references.
References · 5
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
- 2023Automatic Prompt Optimization with "Gradient Descent" and Beam Searchcited by 653
- 2022RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learningcited by 366
- 2023Fairness-guided Few-shot Prompting for Large Language Modelscited by 53
- 2023A Practical Survey on Zero-shot Prompt Design for In-context Learningcited by 49
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.