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

dl.acm.org/doi/10.1145/3706598.3713166

Abstract

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.

Conclusion

Generative AI models, as a design material, offer dynamic capabilities but also constrain software teams working with pre-trained models. In this study, we explored how collaborative teams prototype generative AI applications through prompt engineering, requiring alignment with human-centered values while addressing diverse user needs. Our findings revealed a shift toward content-centric prototyping, where design and computation are intertwined, and prompts become essential tools for shaping both AI behavior and user experience. However, challenges arose, such as implicit assumptions about user behaviors, overfitting models to narrow content, and the sensitivity of generative AI to prompt variations. These issues highlight the risks of exclusion and misalignment between system design and user expectations. We also observed the ethical concerns of using public content for prototyping, emphasizing the need for guidelines. The shifts in prototyping practices call for a broader exploration of design space and new evaluation frameworks that balance both technical feasibility and user experience in generative AI applications.