Human-LLM collaboration in generative design for customization

Generative design enables the rapid creation of diverse designs, making it a promising means for customization. However, due to the multidisciplinary knowledge required for operation, the full potential of generative design for customization (GDfC) remains under-explored. Recently, large language models (LLM) have attracted significant attention from designers. Unlike traditional text-based generative models, LLM’s expansive knowledge base and unique interaction capabilities offer clear advantages for assuming more proactive roles in GDfC. Against the background, this paper explores the potential of LLM in redefining GDfC. Based on the division of the generative design process, this paper identifies three human-LLM collaboration schemes to demonstrate the potential roles of LLM in GDfC. Additionally, this paper proposes a process framework based on the characteristics of required design knowledge, which aids designers in selecting the appropriate LLM performance enhancement strategy for their customization tasks. A case study of vehicle interior customization is presented to demonstrate the application of the proposed framework.