Adopting RAG for LLM-Aided Future Vehicle Design
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929591709335552 |
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| author | Zolfaghari, Vahid Petrovic, Nenad Pan, Fengjunjie Lebioda, Krzysztof Knoll, Alois |
| author_facet | Zolfaghari, Vahid Petrovic, Nenad Pan, Fengjunjie Lebioda, Krzysztof Knoll, Alois |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_09590 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adopting RAG for LLM-Aided Future Vehicle Design Zolfaghari, Vahid Petrovic, Nenad Pan, Fengjunjie Lebioda, Krzysztof Knoll, Alois Software Engineering Artificial Intelligence 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. |
| title | Adopting RAG for LLM-Aided Future Vehicle Design |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2411.09590 |