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arXiv · 2607.01244

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

Andrea Gerardo Russo·Federico Ruggeri·Ivan Tomarchio·Davide Bombini·Nicolò Donati·Gianmarco Pappacoda·Paolo Torroni·Giuseppe-Emiliano La Cara
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Abstract

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.

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Andrea Gerardo Russo, Federico Ruggeri, Ivan Tomarchio, Davide Bombini, Nicolò Donati, Gianmarco Pappacoda, Paolo Torroni, Giuseppe-Emiliano La Cara. 2026-05-18. Retrieval-Augmented Generation to Support Railways Engineering Tasks: A Case Study. https://arxiv.org/abs/2607.01244

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