Reflexive Prompt Engineering: A Framework for Responsible Prompt Engineering and Interaction Design
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Abstract (arXiv)
Responsible prompt engineering has emerged as a critical framework for ensuring that generative artificial intelligence (AI) systems serve society's needs while minimizing potential harms. As generative AI applications become increasingly powerful and ubiquitous, the way we instruct and interact with them through prompts has profound implications for fairness, accountability, and transparency. This article examines how strategic prompt engineering can embed ethical and legal considerations and societal values directly into AI interactions, moving beyond mere technical optimization for functionality. This article proposes a comprehensive framework for responsible prompt engineering that encompasses five interconnected components: prompt design, system selection, system configuration, performance evaluation, and prompt management. Drawing from empirical evidence, the paper demonstrates how each component can be leveraged to promote improved societal outcomes while mitigating potential risks. The analysis reveals that effective prompt engineering requires a delicate balance between technical precision and ethical consciousness, combining the systematic rigor and focus on functionality with the nuanced understanding of social impact. Through examination of real-world and emerging practices, the article illustrates how responsible prompt engineering serves as a crucial bridge between AI development and deployment, enabling organizations to fine-tune AI outputs without modifying underlying model architectures. This approach aligns with broader "Responsibility by Design" principles, embedding ethical considerations directly into the implementation process rather than treating them as post-hoc additions. The article concludes by identifying key research directions and practical guidelines for advancing the field of responsible prompt engineering.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 29 of its 131 references that are in the map. First posted on arXiv 22 Apr 2025.
Builds on
29 of 131 references.
References · 29
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17 more
- 2024Design Principles for Generative AI Applicationscited by 57
- 2024A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Taskscited by 47
- 2024Prompt Design and Engineering: Introduction and Advanced Methodscited by 45
- 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generationcited by 36
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- 2024Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teamscited by 16
- 2024Multilingual Text-to-Image Generation Magnifies Gender Stereotypes and Prompt Engineering May Not Help Youcited by 15
- 2023Requirements Engineering using Generative AI: Prompts and Prompting Patternscited by 13
- 2024Wordflow: Social Prompt Engineering for Large Language Modelscited by 9
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- 2024From Prompt Engineering to Prompt Craftcited by 2
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Citing papers per year: 2025: 2.
2 papers.