From prompt engineering to prompt design: Research strategies for visual generative AI

This demo introduces prompt design as a research approach to studying visual generative AI, distinguishing it from prompt engineering practices that focus on producing aesthetically pleasing or technically polished outputs. Drawing on the query design framework from digital methods, we outline several strategies for cultural and social research: ambiguous prompting for bias research; comparative prompting for cross-model and cross-term analysis; evocative prompting for probing model logic and training data; provocative prompting for examining content moderation; and reverse-engineered prompting for machine critique. We illustrate these strategies through a series of experiments using biodiversity as a case study, examining how visual generative AI represents this concept across models, geographical contexts, and time (2023–2024). These experiments reveal recurring patterns in AI-generated imagery, including idealized, aesthetically driven depictions and the persistence of distinct model “house styles” over time. We further explore the use of large language models as research assistants for analyzing AI-generated images, a process characterized by iterative, supervised collaboration rather than full automation. Finally, we position prompt design as a critical and interventionist method that not only audits but also engages with and reshapes generative AI systems, foregrounding their extractive dynamics and opening possibilities for more reflexive and participatory forms of AI research.