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.

翻译：本文探讨了将大型语言模型与检索增强生成技术相结合，以提升汽车行业自动化设计与软件开发水平。我们提出了两个案例研究：标准化合规聊天机器人和设计协同助手，两者均利用RAG技术提供精准的上下文感知响应。我们评估了四种LLM模型——GPT-4o、LLAMA3、Mistral和Mixtral——比较了它们的回答准确率和执行时间。实验结果表明，虽然GPT-4展现出最优性能，但LLAMA3和Mistral在本地化部署方面也显示出潜力，能够有效应对汽车应用中的数据隐私问题。本研究凸显了RAG增强型LLM在改进汽车工程设计流程与合规性方面的应用前景。