  Developing domain-specific conversational agents (CAs) has been challenged by
the need for extensive domain-focused data. Recent advancements in Large
Language Models (LLMs) make them a viable option as a knowledge backbone. LLMs
behaviour can be enhanced through prompting, instructing them to perform
downstream tasks in a zero-shot fashion (i.e. without training). To this end,
we incorporated structural knowledge into prompts and used prompted LLMs to
build domain-specific voice-based CAs. We demonstrate this approach for the
specific domain of textile circularity in form of the design, development, and
evaluation of TextileBot. We present the design and development of the voice
agent TextileBot and also the insights from an in-person user study (N=30)
evaluating three variations of TextileBots. We analyse the human-agent
interactions, combining quantitative and qualitative methods. Our results
suggest that participants engaged in multi-turn conversations, and their
perceptions of the three variation agents and respective interactions varied
demonstrating the effectiveness of our prompt-based LLM approach. We discuss
the dynamics of these interactions and their implications for designing future
voice-based CAs. The results show that our method's potential for building
domain-specific CAs. Furthermore, most participants engaged in multi-turn
conversations, and their perceptions of the three voice agents and respective
interactions varied demonstrating the effectiveness of our prompt-based LLM
approach. We discuss the dynamics of these interactions and their implications
for designing future voice-based CAs.
