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Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teams

arXiv 2402.17721 ↗First posted 27 Feb 2024cs.HC cs.SEcited by 16

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Abstract (arXiv)

Generative AI models are increasingly being integrated into human task workflows, enabling the production of expressive content across a wide range of contexts. Unlike traditional human-AI design methods, the new approach to designing generative capabilities focuses heavily on prompt engineering strategies. This shift requires a deeper understanding of how collaborative software teams establish and apply design guidelines, iteratively prototype prompts, and evaluate them to achieve specific outcomes. To explore these dynamics, we conducted design studies with 39 industry professionals, including UX designers, AI engineers, and product managers. Our findings highlight emerging practices and role shifts in AI system prototyping among multistakeholder teams. We observe various prompting and prototyping strategies, highlighting the pivotal role of to-be-generated content characteristics in enabling rapid, iterative prototyping with generative AI. By identifying associated challenges, such as the limited model interpretability and overfitting the design to specific example content, we outline considerations for generative AI prototyping.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Feb 2024.

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Citing papers per year: 2025: 11; 2026: 5.

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  1. 2025Vibe Coding as a Reconfiguration of Intent Mediation in Software Development: Definition, Implications, and Research Agendacited by 13
  2. 2025A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomycited by 9
  3. 2025Code Roulette: How Prompt Variability Affects LLM Code Generationcited by 4
  4. 2025Vibe Coding in Product Teams: Reconfiguring AI-Assisted Workflows, Prototyping, and Collaborationcited by 3
  5. 2026Are We All Using Agents the Same Way? An Empirical Study of Core and Peripheral Developers Use of Coding Agentscited by 3
  6. 2025Reflexive Prompt Engineering: A Framework for Responsible Prompt Engineering and Interaction Designcited by 2
  7. 2025Understanding User Experiences of Computer Use Agents: Design Space and Opportunities for Building Agent UX Prototypescited by 2
  8. 2025Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspectivecited by 2
  9. 2025An Exploration of Default Images in Text-to-Image Generationcited by 1
  10. 2025Botender: Supporting Communities in Collaboratively Designing AI Agents through Case-Based Provocationscited by 1
  11. 2025Data-Prompt Co-Evolution: Growing Test Sets to Refine LLM Behaviorcited by 1
  12. 2026Situated, Dynamic, and Subjective: Envisioning the Design of Theory-of-Mind-Enabled Everyday AI with Industry Practitionerscited by 1
4 more
  1. 2026Beyond the Desk: Barriers and Future Opportunities for AI to Assist Scientists in Embodied Physical Taskscited by 1
  2. 2025AI and Agile Software Development: A Research Roadmap from the XP2025 Workshopcited by 0
  3. 2026LLM-Powered Workflow Optimization for Multidisciplinary Software Development: An Automotive Industry Case Studycited by 0
  4. 2026Does AI Save Time on Product Design? A Randomized Controlled Experiment of AI Prompt-to-Design Workflowscited by 0