From Prompt Optimisation to Workflow Architecture: A Comparative Study of Human-AI Workflow Systems in Generative Urban Design

초록

Image-based generative AI is rapidly entering urban design practice, yet it remains unclear whether its limitations can be addressed through prompt refinement or whether they reflect properties of the generation architecture itself. This study examines this question through a comparative case study of two urban design studio tasks under contrasting constraint conditions, drawing on 151 prompt episodes across 20 team projects. In concept-driven tasks, generative AI primarily supported divergence and visual exploration, while morphological bias was partly managed through curatorial selection and reference anchoring. In site-specific tasks, however, outputs often achieved visual plausibility while failing to preserve relational spatial logic, including circulation hierarchy, adjacency dependencies, and boundary conditions. The findings distinguish element-level errors, recoverable through post-editing, from system-level errors, which tended to resist repair within the observed image-generation workflows. Across cases, a three-stage human-AI workflow emerged as a control mechanism: human-led structuring, AI-driven generation and enrichment, and critical curation and refinement. The study contributes to research on human-AI systems by showing that effective integration in constraint-intensive urban design can depend less on prompt optimisation than on workflow architecture, particularly the allocation of validation, generation, and repair functions between human and AI components.

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