# Adopting RAG for LLM-Aided Future Vehicle Design

arXiv: https://arxiv.org/abs/2411.09590 · HTML: https://mlsanity.com/paper/2411.09590/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 14 Nov 2024.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 5 of 5)

- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Safety Analysis in the Era of Large Language Models: A Case Study of STPA using ChatGPT](https://mlsanity.com/paper/2304.01246/) (2023)
- [Welcome Your New AI Teammate: On Safety Analysis by Leashing Large Language Models](https://mlsanity.com/paper/2403.09565/) (2024)
- [Synergy of Large Language Model and Model Driven Engineering for Automated Development of Centralized Vehicular Systems](https://mlsanity.com/paper/2404.05508/) (2024)
- [Towards Single-System Illusion in Software-Defined Vehicles -- Automated, AI-Powered Workflow](https://mlsanity.com/paper/2403.14460/) (2024)

## Built on by (most cited first, 6 of 6)

- [Survey of GenAI for Automotive Software Development: From Requirements to Executable Code](https://mlsanity.com/paper/2507.15025/) (2025)
- [GenAI for Automotive Software Development: From Requirements to Wheels](https://mlsanity.com/paper/2507.18223/) (2025)
- [LLM-based Iterative Approach to Metamodeling in Automotive](https://mlsanity.com/paper/2503.05449/) (2025)
- [LLM-Empowered Event-Chain Driven Code Generation for ADAS in SDV systems](https://mlsanity.com/paper/2511.21877/) (2025)
- [LLM-Empowered Functional Safety and Security by Design in Automotive Systems](https://mlsanity.com/paper/2601.02215/) (2026)
- [Think Inside the Chunk: RegulaRAG for Regulation-Compliant Scenario Generation using LLMs: A Case Study of UN Regulation No. 152](https://mlsanity.com/paper/2608.16394/) (2026)

## Abstract (arXiv)

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.