[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83575-en":3,"doc-seo-83575-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83575,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Retrieval-Augmented Generation to Support Railways Engineering Tasks: A Case Study","The growing number and complexity of technical regulations create major challenges for professionals operating in regulated industries. This paper presents a case study, from design to deployment, of a Retrieval-Augmented Generation system for consulting complex railway-domain regulations. While built for railways, the approach offers industrial value for any domain needing regulatory compliance and accurate retrieval from dense documentation. The work emphasizes a human-centered implementation of LLM-based technical documentation consultation, aligning system capabilities with domain expertise.","RETRIEVAL-AUGMENTED GENERATION TO SUPPORT RAILWAYS ENGINEERING TASKS: A CASE STUDY  \narXiv :2607 .01244v1 [ cs .IR] 18 May 2026  \nAndrea Gerardo Russo  \nNIER Engineering S.p.A. [a.russo@nier.it](a.russo@nier.it)  \nDavide Bombini  \nNIER Engineering S.p.A. [d.bombini@nier.it](d.bombini@nier.it)  \nFederico Ruggeri  \nUniversity of Bologna [federico.ruggeri6@unibo.it](federico.ruggeri6@unibo.it)  \nIvan Tomarchio  \nNIER Engineering S.p.A. [i.tomarchio@nier.it](i.tomarchio@nier.it)  \nNicolò Donati  \nUniversity of Bologna [n.donati@unibo.it](n.donati@unibo.it)  \nGianmarco Pappacoda  \nUniversity of Bologna [gianmarco.pappacoda@unibo.it](gianmarco.pappacoda@unibo.it)  \nPaolo Torroni  \nUniversity of Bologna [p.torroni@unibo.it](p.torroni@unibo.it)  \nGiuseppe-Emiliano La Cara  \nNIER Engineering S.p.A. [e.lacara@nier.it](e.lacara@nier.it)  \nJuly 3, 2026  \nABSTRACT  \nThe 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.  \n1 Introduction  \nCompanies and industries selling their goods in the European market must often comply with a set of technical standards and regulations aimed at guaranteeing European citizens safe and high-quality products. Over the years, the European Union has issued regulations regarding several aspects in many industrial sectors, such as biomedical [15], chemical [2], automotive [4] and railway [3] . These technical regulations address all aspects of the product life cycle, from design to decommission, in a very detailed manner. They are typically written in highly specialized language and contain lengthy, detailed tables and complex diagrams. Therefore, their consultation and correct interpretation can be time-consuming even for experienced professionals.  \nThe advent of generative unlocked new possibilities. In recent years, LLMs have reported outstanding performance in general question-answering tasks [16] and their results are even better when using in-context learning with task instructions and few-shot demonstrations [14] . However, as they are bound to a fixed knowledge base that cannot be easily updated [23], they cannot provide responses about topics not contained in their training dataset. More importantly, due to their probabilistic nature, it has been observed that LLM-generated responses can be factually incorrect and even nonsensical [25], causing skepticism in business and industrial domains where factual consistency is key [25] . An interesting approach to mitigating these issue is Retrieval-Augmented Generation (RAG) [10], which extends generative models with a retrieval component, whereby document chunks relevant to a given user query are retrieved from an external knowledge base and inserted into an augmented prompt [7] . RAG has been successfully applied in many technical domains such as finance [13] and medicine [24] .  \nA PREPRINT-JULY 3, 2026  \nThis work describes a case study, from design to deployment, of a RAG implementation for the railway sector. The end-users of this framework are engineering professionals whose job is to develop systems that comply with the technical standards and specifications that railway products sold in the European Union are required to comply with, to ensure maximum safety. For example, the railways interoperability defined in the context of the","cbCaidu2tRmEdXS1","https://ap.wps.com/l/cbCaidu2tRmEdXS1","pdf",501031,3,1,13,"English","en",105,"# Introduction\n## Problem context: complex technical regulations\n## LLM limitations and the need for retrieval\n# Development Methodology\n## Requirements definition","[{\"question\":\"What problem does the paper address for railway engineering teams?\",\"answer\":\"The paper addresses the difficulty and time cost of consulting and correctly interpreting highly specialized railway technical standards and specifications, which include complex tables and diagrams.\"},{\"question\":\"Why is Retrieval-Augmented Generation used instead of a standalone LLM?\",\"answer\":\"Because LLMs have a fixed knowledge base and can produce factually incorrect or nonsensical answers, RAG retrieves relevant document chunks and grounds responses in an external knowledge source.\"},{\"question\":\"How is the proposed system developed 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