Anatomy of a Prompt: a Semiotic System of Text-to-Image Gen AI
DOI:
https://doi.org/10.26375/disegno.16.2025.24Keywords:
drawing, semiotic system, Generative AI, prompt, visual languageAbstract
Text-to-image Generative AI (Gen AI) introduces a novel perspective on the notion of drawing as a language. This study employs Saussure’s Semiotic Theory to investigate how architectural and interior design prompts in DiffusionDB (part-000001) function as a semiotic system. The filtered dataset includes 246 architecture-related prompts (APs) and 276 interior design-related prompts (IDPs), validated through random sampling and visual inspection. Among the APs, 98% featured terms like ‘architecture’ and ‘design’ with ‘building’, ‘nature’, and ‘archdaily’ appearing in 71-72%. Other prevalent themes included ‘city’ (59%), ‘art’, and ‘digital’ (48%), reflecting a conceptual emphasis on physical structures integrated with environmental and stylistic elements. In contrast, IDPs showed a 100% occurrence of ‘style’, followed by ‘light’ (74%) and ‘concept’ (63%), suggesting a focus on stylistic expression and ambient qualities. The findings indicate that APs center around ‘building’ as the core term, while IDPs are anchored by ‘style’.
Notably, ArchDaily and Artstation emerged as linguistic and visual archives, informing the prompt structures. This semiotic analysis reveals that Gen AI users consciously employ architectural vocabulary and artistic techniques, crafting prompts as intentional design compositions that bridge language, imagery, and social meanings in architecture and interior design.
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