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-测试循环是创新的关键环节，但物理原型制作往往耗时且成本高昂。尽管基于物理的仿真和策略性原型制作能够降低成本，但通常需等到集成原型建成后才能进行有意义的评估。本文探讨了生成式预训练Transformer（GPT）能否预测通常需通过原型制作获取的信息，包括成本、性能及感知可用性。我们提出一种检索增强生成（RAG）方法，利用基于Instructables.com采集的原型数据增强相关先例的可及性，通过OpenAI GPT-4o模拟设计反馈。研究包含两项实验：首先通过受控实验比较GPT-RAG与人类设计师在接收设计草图后对成本、性能及可用性的预测效果，并以物理原型的真实结果作为评估基准；其次展示应用性案例，根据GPT-RAG建议制作物理原型，并将其与商业化基准设计及拓扑优化设计进行对比。结果表明：GPT-RAG在成本与性能评估上比个人或群体人类评估更准确，同时能提供相当的可用性见解；基于GPT-RAG建议制作的原型在性能上也优于两个对比原型。通过重复查询与响应平均能显著提升预测精度，这表明大语言模型能够模拟符合大数定律的群体聚合效应。