Un-trusting the Chat: Designing for Calibrated Trust in Retrieval-Augmented Conversations
Abstract
Retrieval-Augmented Generation (RAG) systems are increasingly used in conversational AI to support workplace decision-making. Yet in contexts marked by time pressure or ambiguous information, users risk over-relying on system outputs they cannot fully assess. This paper explores how interface design can support calibrated trust—an alignment between user confidence and system reliability—in RAG-based chat interfaces. Through a co-design workshop with HCI and AI experts, we investigated interaction strategies that promote transparency without overwhelming users. Participants proposed design features such as color-coded source references, non-numeric relevance indicators, and adaptive language for expressing uncertainty. These insights reveal how conversational interfaces can communicate limitations in context-sensitive and user-centered ways. We contribute preliminary design directions for trustworthy generative systems and outline next steps for implementation and evaluation.
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