预测性原型：通过ChatGPT评估设计概念
Predictive Prototyping: Evaluating Design Concepts with ChatGPT
AI总结：
本文通过GPT-RAG方法评估设计概念，证明其在预测成本、性能和可用性方面优于人类估计，并展示了由该方法指导的原型在性能上的优势。
摘要：
设计-构建-测试循环对于创新至关重要，但物理原型往往速度慢且成本高。尽管基于物理的模拟和战略原型可以降低成本，但有意义的评估通常要等到集成原型建成后才受到限制。本文研究了一种生成性预训练变换器（GPT）是否能够预测通过原型获得的信息，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抓取的原型数据来增加相关先例的可访问性。两项研究被报告。首先，一个受控实验比较了GPT-RAG和人类设计师，后者收到设计草图并预测成本、性能和可用性；预测结果与物理原型的真实结果进行评估。其次，我们报告了一个应用演示，其中从GPT-RAG建议中制作出物理原型，并与商业基线和拓扑优化设计进行比较。结果表明，GPT-RAG提供的成本和性能估计比个体或大众人类估计更准确，同时产生可比的可用性见解；由GPT-RAG指导的原型也优于两者比较原型。重复查询并响应平均显著提高了准确性，表明LLMs可以模拟大众聚合效应，与大数定律一致。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
