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Ai-driven decision support in the automotive industry: designing a user-centric ai chatbot using large language models and the double diamond approach
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Resumo(s)
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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Palavras-chave
Ai-driven decision-making User-centric design Chatbot development Large language models Prompt engineering Retrieval-augmented generation