Design ideation through large language model-driven design operation: a case study of architectural design using pattern language

Abstract

This research proposes a systematic method for design ideation with large language models (LLMs), grounded in the design operation model inspired by Christopher Alexander’s pattern language (PL). Design operation refers here to a tree of thought for the step-by-step definition of the design context behind a design problem, the functional requirements, the physical attributes of alternative solutions and the generation of design alternatives that synthesize those attributes. To examine how the design operation improves LLM performance for design ideation, we implemented an architectural design ideation case study and compared four prompt methods for generating design alternatives. The four prompt methods are designed with and without the design operation and the PL. Respective prompts generated 100 design alternatives which were evaluated both by human experts and through computational diversity metrics. The results showed that prompts using the PL tend to generate design alternatives whose creativity is highly rated by humans, but are strongly influenced by the given knowledge and lack diversity, whereas prompts incorporating the design operation have the potential to enhance validity and feasibility and attribute diversity.