mlsanityData to 14 Oct 2026

Retrieval-Augmented Generation to Support Railways Engineering Tasks: A Case Study

arXiv 2607.01244 ↗First posted 18 May 2026cs.IR cs.CYcited by 0

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

Abstract (arXiv)

The growing number and complexity of technical regulations represent an important challenge for all professionals in regulated industries. This paper describes a case study, from design to deployment, of building a Retrieval-Augmented Generation system for the consultation of complex technical regulations in the railway domain. Although developed for the railway sector, this testimony of an industrial experience is of particular value for technical domains where regulatory compliance and accurate information retrieval from complex documentation are essential requirements. It also constitutes a human-centered approach for implementing LLM-powered technical documentation consultation across various regulated industries, balancing technological capabilities with domain expertise.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 16 of its 23 references that are in the map. First posted on arXiv 18 May 2026.

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16 of 23 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective21 Nov
  2. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 14

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2019Language Models as Knowledge Bases?cited by 1,936
  4. 2020MPNet: Masked and Permuted Pre-training for Language Understandingcited by 981
  5. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 332
  6. 2024RAFT: Adapting Language Model to Domain Specific RAGcited by 262
  7. 2024Continual Learning for Large Language Models: A Surveycited by 147
  8. 2024Evaluating Quantized Large Language Modelscited by 77
  9. 2023FinGPT: Democratizing Internet-scale Data for Financial Large Language Modelscited by 67
  10. 2024Exploiting LLM Quantizationcited by 65
  11. 2024Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questionscited by 54
  12. 2023Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learningcited by 16
2 more
  1. 2024Tabular Embedding Model (TEM): Finetuning Embedding Models For Tabular RAG Applicationscited by 8
  2. 2025Infinite Retrieval: Attention Enhanced LLMs in Long-Context Processingcited by 7

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  2. 2025Evaluating Retrieval Augmented Generative Models for Document Queries in Transportation Safety
  3. 2025Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation
  4. 2026An Industrial-Scale Retrieval-Augmented Generation Framework for Requirements Engineering: Empirical Evaluation with Automotive Manufacturing Data
  5. 2025RAGuard: A Novel Approach for in-context Safe Retrieval Augmented Generation for LLMs
  6. 2025Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps
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  8. 2023Retrieval Augmented Generation using Engineering Design Knowledge