AI & ML
Retrieval-Augmented Generation to Support Railways Engineering Tasks: A Case Study
arXiv cs.IR0 views
arXiv:2607.01244v2 Announce Type: replace 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.
Source:https://arxiv.org/abs/2607.01244PreviousFrom Clicks to Intent: Cross-Platform Session Embeddings with LLM-Distilled Taxonomy for Financial Services RecommendationsNextQASP: Query-Adaptive Robust Vector Search Policy
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