Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teams
February 27, 2024 Β· Declared Dead Β· π International Conference on Human Factors in Computing Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Hari Subramonyam, Divy Thakkar, Andrew Ku, JΓΌrgen Dieber, Anoop Sinha
arXiv ID
2402.17721
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.SE
Citations
30
Venue
International Conference on Human Factors in Computing Systems
Last Checked
4 months ago
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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted