Customization and personalization of large language models for engineering design
Year: 2025
Type: article
Abstract: 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.
Source: CIRP Annals
Authors: Zhoumingju Jiang, Ang Liu, Dawen Zhang, Xiwei Xu, Yun Dai
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,190
OpenAlex ID: W4409125299