mlsanityData to 7 Oct 2026

An Industrial-Scale Retrieval-Augmented Generation Framework for Requirements Engineering: Empirical Evaluation with Automotive Manufacturing Data

arXiv 2603.20534 ↗First posted 20 Mar 2026cs.SE cs.AIcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

Requirements engineering in Industry 4.0 faces critical challenges with heterogeneous, unstructured documentation spanning technical specifications, supplier lists, and compliance standards. While retrieval-augmented generation (RAG) shows promise for knowledge-intensive tasks, no prior work has evaluated RAG on authentic industrial RE workflows using comprehensive production-grade performance metrics. This paper presents a comprehensive empirical evaluation of RAG for industrial requirements engineering automation using authentic automotive manufacturing documentation comprising 669 requirements across four specification standards (MBN 9666-1, MBN 9666-2, BQF 9666-5, MBN 9666-9) spanning 2015-2023, plus 49 supplier qualifications with extensive supporting documentation. Through controlled comparisons with BERT-based and ungrounded LLM approaches, the framework achieves 98.2% extraction accuracy with complete traceability, outperforming baselines by 24.4% and 19.6%, respectively. Hybrid semantic-lexical retrieval achieves MRR of 0.847. Expert quality assessment averaged 4.32/5.0 across five dimensions. The evaluation demonstrates 83% reduction in manual analysis time and 47% cost savings through multi-provider LLM orchestration. Ablation studies quantify individual component contributions. Longitudinal analysis reveals a 55% reduction in requirement volume coupled with 1,800% increase in IT security focus, identifying 10 legacy suppliers (20.4%) requiring requalification, representing potential 2.3M in avoided contract penalties.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 7 of its 22 references that are in the map. First posted on arXiv 20 Mar 2026.

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  1. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 6

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  4. 2023LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Modelscited by 279
  5. 2021Retrieval Augmented Code Generation and Summarizationcited by 148
  6. 2018Traceability in the Wild: Automatically Augmenting Incomplete Trace Linkscited by 4

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  3. 2025Towards Requirements Engineering for RAG Systems
  4. 2025Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation
  5. 2025An Agile Method for Implementing Retrieval Augmented Generation Tools in Industrial SMEs
  6. 2024Generating Test Scenarios from NL Requirements using Retrieval-Augmented LLMs: An Industrial Study
  7. 2024Adopting RAG for LLM-Aided Future Vehicle Design
  8. 2024RAG Does Not Work for Enterprises