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

arXiv: https://arxiv.org/abs/2607.01244 · HTML: https://mlsanity.com/paper/2607.01244/

## 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.

## Placed in

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

## Builds on (most cited first, 16 of 16)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Hallucination is Inevitable: An Innate Limitation of Large Language Models](https://mlsanity.com/paper/2401.11817/) (2024)
- [RAFT: Adapting Language Model to Domain Specific RAG](https://mlsanity.com/paper/2403.10131/) (2024)
- [Continual Learning for Large Language Models: A Survey](https://mlsanity.com/paper/2402.01364/) (2024)
- [Evaluating Quantized Large Language Models](https://mlsanity.com/paper/2402.18158/) (2024)
- [FinGPT: Democratizing Internet-scale Data for Financial Large Language Models](https://mlsanity.com/paper/2307.10485/) (2023)
- [Exploiting LLM Quantization](https://mlsanity.com/paper/2405.18137/) (2024)
- [Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions](https://mlsanity.com/paper/2408.00727/) (2024)
- [Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective](https://mlsanity.com/paper/2411.14572/) (2024)
- [Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning](https://mlsanity.com/paper/2311.11551/) (2023)
- [Tabular Embedding Model (TEM): Finetuning Embedding Models For Tabular RAG Applications](https://mlsanity.com/paper/2405.01585/) (2024)
- [Infinite Retrieval: Attention Enhanced LLMs in Long-Context Processing](https://mlsanity.com/paper/2502.12962/) (2025)

## 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.