  Access to large amounts of diverse design solutions can support designers
during the early stage of the design process. In this paper, we explore the
efficacy of large language models (LLM) in producing diverse design solutions,
investigating the level of impact that parameter tuning and various prompt
engineering techniques can have on the diversity of LLM-generated design
solutions. Specifically, LLMs are used to generate a total of 4,000 design
solutions across five distinct design topics, eight combinations of parameters,
and eight different types of prompt engineering techniques, comparing each
combination of parameter and prompt engineering method across four different
diversity metrics. LLM-generated solutions are compared against 100
human-crowdsourced solutions in each design topic using the same set of
diversity metrics. Results indicate that human-generated solutions consistently
have greater diversity scores across all design topics. Using a post hoc
logistic regression analysis we investigate whether these differences primarily
exist at the semantic level. Results show that there is a divide in some design
topics between humans and LLM-generated solutions, while others have no clear
divide. Taken together, these results contribute to the understanding of LLMs'
capabilities in generating a large volume of diverse design solutions and offer
insights for future research that leverages LLMs to generate diverse design
solutions for a broad range of design tasks (e.g., inspirational stimuli).
