Publicação
Ai-driven decision support in the automotive industry: designing a user-centric ai chatbot using large language models and the double diamond approach
| Resumo: | Companies are confronted with a paradoxical situation: although data is abundant, its volume complicates rather than facilitates decision-making. This thesis explores how large language models (LLMs) can address this challenge with a user-centered approach. Based on the specific needs and challenges of decision-makers in the automotive industry, identified through user interviews and content analysis, a chatbot was developed and evaluated. The findings demonstrate the potential of LLMs to streamline decision-making by efficiently processing complex data and generating insights. This research showcases the feasibility of user-centered AI tools in enhancing decision-making processes and provides a comprehensive framework for future research. |
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| Autores principais: | Preiss, Florian Fritz |
| Assunto: | Ai-driven decision-making User-centric design Chatbot development Large language models Prompt engineering Retrieval-augmented generation |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | Companies are confronted with a paradoxical situation: although data is abundant, its volume complicates rather than facilitates decision-making. This thesis explores how large language models (LLMs) can address this challenge with a user-centered approach. Based on the specific needs and challenges of decision-makers in the automotive industry, identified through user interviews and content analysis, a chatbot was developed and evaluated. The findings demonstrate the potential of LLMs to streamline decision-making by efficiently processing complex data and generating insights. This research showcases the feasibility of user-centered AI tools in enhancing decision-making processes and provides a comprehensive framework for future research. |
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