In this paper, we explore the integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to enhance automated design and software development in the automotive industry. We present two case studies: a standardization compliance chatbot and a design copilot, both utilizing RAG to provide accurate, context-aware responses. We evaluate four LLMs-GPT-4o, LLAMA3, Mistral, and Mixtral- comparing their answering accuracy and execution time. Our results demonstrate that while GPT-4 offers superior performance, LLAMA3 and Mistral also show promising capabilities for local deployment, addressing data privacy concerns in automotive applications. This study highlights the potential of RAG-augmented LLMs in improving design workflows and compliance in automotive engineering.

本文探讨了大型语言模型（LLMs）与检索增强生成（RAG）的结合，以提升汽车行业的自动化设计和软件开发效率。通过两个案例研究，展示了RAG在标准化合规聊天机器人和设计助手中的应用，结果表明GPT-4在性能上表现出色，同时LLAMA3和Mistral在本地部署方面也显示出良好的潜力，从而有效解决了汽车应用中的数据隐私问题。

采用RAG技术进行大型语言模型辅助未来车辆设计