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The Impact of LLMs on Product Structure Representation: Challenges and Opportunities in the Solutions Business
Ganze, Nele; Krüger, Lorenz; Jakschik, Stefan; Paetzold-Byhain, Kristin
Type:
Year:
2026
Editor:
Paetzold-Byhain, K.; Krause, D.; Wartzack, S. and Koch, A.
Author:
Series:
DfX
Institution:
ULT AG, Löbau, Germany; VON ARDENNE GmbH, Dresden, Germany; Dresden University of Technology, Dresden, Germany
Page(s):
090-099
DOI number:
Abstract:
Companies in the solutions business must efficiently translate customer inquiries into feasible solutions – supported by Large Language Modells (LLMs) through configuration assistance and semantic similarity search. However, leveraging LLMs requires product structures to be provided in a suitable form for Retrieval-Augmented Generation (RAG). This paper presents a framework for selecting a provisioning method – vector database, knowledge graph, or relational database. Requirements are analyzed from two perspectives: the technical constraints of the LLM and the characteristics of the existing product structure and enterprise context. A weighted heatmap operationalizes the selection. The framework was iteratively developed with two companies from the solutions business.
Keywords:
