Retrieval-Augmented Generation to Support Railways Engineering Tasks: A Case Study
It provides an industrial experience report for regulated industries needing accurate information retrieval from complex documentation, though the results are incremental.
The paper presents a case study of building a Retrieval-Augmented Generation system for consulting complex railway technical regulations, demonstrating a human-centered approach that balances LLM capabilities with domain expertise.
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.