Analyzing Data from Urban Citizen Participation by Applying the Retrieval Augmented Generation Architecture

This study explores the application of the retrieval-augmented generation architecture for large language models in analyzing citizens' contributions from urban participation. Existing literature highlights the potential of large language models to streamline analytical processes. However, challenges regarding required functions, domain expertise, and transparency remain underexplored. This research addresses these issues through a design science research approach. We identified eleven issues with a systematic literature review and twelve expert interviews, formulated twelve meta-requirements, and derived four design principles on which we developed a web prototype. We evaluated it with 42 experts from a crowdsourcing platform. Our findings demonstrate that retrieval-augmented generation models can enhance efficiency in automated categorization, sentiment analysis, and summarization by focusing the model's attention. However, transparency limitations persist as an ongoing challenge. Our findings contribute to existing knowledge by illustrating how hybrid intelligent systems can improve urban experts' ability to analyze and interpret participation data in smart cities.