arXiv cs.CY· Andrea Gerardo Russo, Federico Ruggeri, Ivan Tomarchio, Davide Bombini, Nicol\`o Donati, Gianmarco Pappacoda, Paolo Torroni, Giuseppe-Emiliano La Cara·· 3 天前AI 评分21
铁路工程任务中的检索增强生成：一个案例研究
Retrieval-Augmented Generation to Support Railways Engineering Tasks: A Case Study
AI 导读
一项案例研究记录了面向铁路领域复杂技术法规咨询的 RAG 系统从设计到部署的全过程。该系统以人为中心，用 LLM 驱动技术文档咨询，在技术能力与领域专业知识之间取得平衡，其经验对监管合规要求高、需从复杂文档中精准检索信息的受监管行业具有参考价值。
正文 · 原文
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.
| Subjects: | Information Retrieval (cs.IR); Computers and Society (cs.CY) |
| Cite as: | arXiv:2607.01244 [cs.IR] |
| (or arXiv:2607.01244v2 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2607.01244 arXiv-issued DOI via DataCite |
Submission history
From: Federico Ruggeri [view email]
[v1]
Mon, 18 May 2026 14:57:21 UTC (288 KB)
[v2]
Wed, 14 Oct 2026 13:26:13 UTC (706 KB)
来源：arXiv cs.CY · arxiv.org