This study evaluates the efficacy of various freely available Large Language Models (LLMs) in conducting semi-automated purpose-oriented technology searches to support design activities as well as Technology Intelligence for innovation management, using a systematic manual search as a baseline for comparison. The case to run the comparison focuses on identifying water purification technologies suitable for mobile systems. The results show that LLMs can target more technologies than human-based searches, reducing time demands and providing wider entry points for additional technology analysis.

Evaluating large language models for technology-oriented searches in engineering design

Koutsouvelis, Vasileios;Becattini, Niccolo'
2026-01-01

Abstract

This study evaluates the efficacy of various freely available Large Language Models (LLMs) in conducting semi-automated purpose-oriented technology searches to support design activities as well as Technology Intelligence for innovation management, using a systematic manual search as a baseline for comparison. The case to run the comparison focuses on identifying water purification technologies suitable for mobile systems. The results show that LLMs can target more technologies than human-based searches, reducing time demands and providing wider entry points for additional technology analysis.
2026
Proceedings of the Design Society
artificial intelligence (AI); design strategy; early design phase; information retrieval; supportive technologies;
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1322227
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