Lessons from A Large Language Model-based Outdoor Trail Recommendation Chatbot with Retrieval Augmented Generation
This work addresses the demand for conversational AI in outdoor recreation by offering a practical solution, though it appears incremental as it applies existing RAG methods to a new domain.
The paper tackled the problem of providing accurate and personalized outdoor trail recommendations via conversational AI by developing Judy, a chatbot using a large language model with retrieval augmented generation, and demonstrated its accuracy, effectiveness, and usability through case studies in Connecticut.
The increasing popularity of outdoor recreational activities (such as hiking and biking) has boosted the demand for a conversational AI system to provide informative and personalized suggestion on outdoor trails. Challenges arise in response to (1) how to provide accurate outdoor trail information via conversational AI; and (2) how to enable usable and efficient recommendation services. To address above, this paper discusses the preliminary and practical lessons learned from developing Judy, an outdoor trail recommendation chatbot based on the large language model (LLM) with retrieval augmented generation (RAG). To gain concrete system insights, we have performed case studies with the outdoor trails in Connecticut (CT), US. We have conducted web-based data collection, outdoor trail data management, and LLM model performance studies on the RAG-based recommendation. Our experimental results have demonstrated the accuracy, effectiveness, and usability of Judy in recommending outdoor trails based on the LLM with RAG.