Customization and personalization of large language models for engineering design

Large language models (LLMs) are increasingly used in design and manufacturing, yet directly employing general-purpose LLMs for conceptual design often leads to unmanufacturable concepts. This paper aims to adapt general-purpose LLMs for design-specific tasks. A new framework is presented to customize a general-purpose LLM into a design-specific model based on design-relevant data and Retrieval-Augmented Generation (RAG). Another complementary framework is presented to personalize the design-specific LLM by integrating design reasoning with prompting techniques. A design experiment, using patent documents as the design-relevant data, demonstrates that customization and personalization can improve LLM effectiveness in conceptual design, especially by enhancing concept feasibility.