# Lessons from A Large Language Model-based Outdoor Trail Recommendation Chatbot with Retrieval Augmented Generation

arXiv: https://arxiv.org/abs/2508.05652 · HTML: https://mlsanity.com/paper/2508.05652/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RAG for recommendation, one of 15 directions in Retrieval-augmented generation.
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 27 Jul 2025.

## Placed in

- Retrieval-augmented generation / [RAG for recommendation](https://mlsanity.com/rag/rag-for-recommendation/)

## Builds on (most cited first, 3 of 3)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)

## Abstract (arXiv)

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.