Expanding the Generative Power of Large Language Models (LLMs) for Design through Formal Design Grammars and Languages

Abstract Research in design grammars has been underway for over 50 years and has demonstrated great generative power for a wide range of design and engineering domains. A key limitation, though, is the lack of support for designers to develop and computationally implement formal design grammars. We explore the potential of Large Language Models (LLMs) to act as a collaborative grammar development partner that works with human designers and provides guidance during grammar development as well as serving as a grammar interpreter that converts natural language descriptions of design grammars into executable Python code. Methods for both interpreting previously known design grammars as well as interactively and collaboratively developing a new design grammar that is not known a priori are proposed. Three case studies, namely a truss design grammar, half-hexagon shape grammar and a technical process grammar, are investigated covering string, shape and graph grammars to explore the advantages and limitations of combining design grammars and LLMs. Finally, we position formal design grammars to be a key element for the future to expand the generative power of LLMs and enable them to become more repeatable, precise and explainable for generative design tasks.