Large Language Models for Computer-Aided Design: A Survey

Large Language Models (LLMs) have seen rapid advancements in recent years, with models like ChatGPT and DeepSeek, showcasing their remarkable capabilities across diverse domains. While substantial research has been conducted on LLMs in various fields, a comprehensive review focusing on their integration with Computer-Aided Design (CAD) remains notably absent. CAD is the industry standard for 3D modeling and plays a vital role in the design and development of products across different industries. As the complexity of modern designs increases, the potential for LLMs to enhance and streamline CAD workflows presents an exciting frontier. This article presents the first systematic survey exploring the intersection of LLMs and CAD. We begin by outlining the industrial significance of CAD, highlighting the need for Artificial Intelligence (AI)-driven innovation. Next, we provide a detailed overview of the foundation of LLMs. We also examine both closed-source LLMs as well as publicly available models. The core of this review focuses on the various applications of LLMs in CAD, providing a taxonomy of six key areas where these models are making considerable impact. We also provide a comprehensive study of CAD evaluation, reviewing existing methods and metrics in detail. In our analysis, we also examine common data modalities, model usage trends, dataset sources, and industrial application domains to provide a well-rounded picture of the field. Finally, we propose several promising future directions for further advancements, which offer vast opportunities for innovation and are poised to shape the future of CAD technology. Github: https://github.com/lichengzhanguom/LLMs-CAD-Survey-Taxonomy.

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Publication Details

Journal
ACM Computing Surveys
Published
2026-01-06
DOI
https://doi.org/10.1145/3787499
Primary Topic
Machine Learning in Materials Science
Type
article
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article

Large Language Models for Computer-Aided Design: A Survey

Naveed Akhtar, Bach Le, Licheng Zhang, Duc Ngo et al.
ACM Computing Surveys
Machine Learning in Materials Science
article

Large Language Models for Computer-Aided Design: A Survey

Naveed Akhtar, Bach Le, Licheng Zhang, Duc Ngo, Siew-Kei Lam
article en

Abstract

Large Language Models (LLMs) have seen rapid advancements in recent years, with models like ChatGPT and DeepSeek, showcasing their remarkable capabilities across diverse domains. While substantial research has been conducted on LLMs in various fields, a comprehensive review focusing on their integration with Computer-Aided Design (CAD) remains notably absent. CAD is the industry standard for 3D modeling and plays a vital role in the design and development of products across different industries. As the complexity of modern designs increases, the potential for LLMs to enhance and streamline CAD workflows presents an exciting frontier. This article presents the first systematic survey exploring the intersection of LLMs and CAD. We begin by outlining the industrial significance of CAD, highlighting the need for Artificial Intelligence (AI)-driven innovation. Next, we provide a detailed overview of the foundation of LLMs. We also examine both closed-source LLMs as well as publicly available models. The core of this review focuses on the various applications of LLMs in CAD, providing a taxonomy of six key areas where these models are making considerable impact. We also provide a comprehensive study of CAD evaluation, reviewing existing methods and metrics in detail. In our analysis, we also examine common data modalities, model usage trends, dataset sources, and industrial application domains to provide a well-rounded picture of the field. Finally, we propose several promising future directions for further advancements, which offer vast opportunities for innovation and are poised to shape the future of CAD technology. Github: https://github.com/lichengzhanguom/LLMs-CAD-Survey-Taxonomy.

ACM Computing SurveysVol. 58(9)
The University of Melbourne (AU), Nanyang Technological University (SG)
Industry, innovation and infrastructure
Openalex Percentile: Top 95%
Machine Learning in Materials Science
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