People have long asked complex existential and ethical questions, e.g., ‘What is a meaningful life?’ or ‘How can I address inequality?’ Theological texts offer deep insights on these critical questions, but their complexity often limits their access to a larger audience. We present a system that uses retrieval-augmented generation (RAG) with large language models (LLMs) to make this wisdom more accessible. By retrieving content from theological sources and synthesizing it into understandable answers, the system addresses a wide range of ethical questions. Technically, we propose ensemble RAG methods with iterative query simplification to align better with the ancient vocabulary. Our application highlights the potential of AI to support ethical reflection.

A video is available here (https://youtu.be/q-woQLs-jXM).