Adopting RAG for LLM-Aided Future Vehicle Design
This work addresses design workflow and compliance challenges for automotive engineers, but it is incremental as it applies existing RAG methods to a new domain.
The paper tackles the problem of automated design and software development in the automotive industry by integrating Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to enhance accuracy and context-awareness. The results show that GPT-4 offers superior performance, while LLAMA3 and Mistral show promising capabilities for local deployment, addressing data privacy concerns.
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.