This presentation outlines the development, challenges, and future plans for a virtual museum tour guide for the COSI Language Pod. Originally derived from the Virtual Patient project, the guide initially relied on a static question-answering system that required frequent retraining and could answer only a limited set of questions. The transition to a more dynamic, retrieval-augmented generation (RAG) model aims to increase responsiveness, robustness, and resource efficiency, with minimal dependency on costly, corporate AI systems. Key development phases include leveraging open-source, mid-sized LLMs and knowledge distillation techniques to balance robustness and control. Key enhancements include exploring retrieval methods, adapting models for multilingual interactions, and ensuring safe, confabulation-free outputs. Future steps involve reducing hallucinations further through contrastive and reinforcement learning and exploring potential adaptations for similar projects.