The design-build-test cycle is essential for innovation, but physical prototyping is often slow and expensive. Although physics-based simulation and strategic prototyping can reduce cost, meaningful evaluation is frequently constrained until an integrated prototype is built. This paper investigates whether a generative pretrained transformer (GPT) can predict information typically obtained through prototyping, including cost, performance, and perceived usability. We introduce a retrieval-augmented generation (RAG) method to emulate design feedback using OpenAI GPT-4o, grounded in prototyping data scraped from Instructables.com to increase access to relevant precedent. Two studies are reported. First, a controlled experiment compares GPT-RAG and human designers, who receive design sketches and predict cost, performance, and usability; predictions are evaluated against ground-truth results from physical prototypes. Second, we report an applied demonstration in which a physical prototype is produced from GPT-RAG recommendations and compared with a commercial baseline and a topology-optimized design. Results show that GPT-RAG provides more accurate cost and performance estimates than individual or crowd human estimates, while yielding comparable usability insights; the GPT-RAG-informed prototype also outperforms both comparison prototypes. Repeated querying with response averaging significantly improves accuracy, suggesting that LLMs can emulate crowd aggregation effects consistent with the law of large numbers.

翻译：设计-构建-测试循环对创新至关重要，但物理原型制作通常缓慢且昂贵。尽管基于物理的仿真和策略性原型制作可以降低成本，但往往需要等到集成原型构建完成后才能进行有意义的评估。本文研究生成式预训练Transformer（GPT）能否预测通常通过原型制作获得的信息，包括成本、性能和感知可用性。我们引入了一种检索增强生成（RAG）方法，利用OpenAI GPT-4o模拟设计反馈，该方法基于从Instructables.com抓取的原型制作数据，以增加对相关先例的访问。报告了两项研究。首先，一项对照实验比较了GPT-RAG与人类设计师，他们接收设计草图并预测成本、性能和可用性；预测结果通过与物理原型的真实结果对比进行评估。其次，我们报告了一项应用演示，其中根据GPT-RAG建议制作了一个物理原型，并与商业基准设计和拓扑优化设计进行了比较。结果表明，GPT-RAG提供的成本和性能估计比个人或群体人类估计更准确，同时产生可比的可用性见解；基于GPT-RAG信息制作的原型也优于两个对比原型。通过重复查询和响应平均显著提高了准确性，这表明大型语言模型能够模拟符合大数定律的群体聚合效应。