There exist challenges in learning and understanding religions as the presence of complexity and depth of religious doctrines and teachings. Chatbots as question-answering systems can help in solving these challenges. LLM chatbots use NLP techniques to establish connections between topics and accurately respond to complex questions. These capabilities make it perfect to be used in enlightenment on religion as a question answering chatbot. However, LLMs also have a tendency to generate false information, known as hallucination. The responses of the chatbots can include content that insults personal religious beliefs, interfaith conflicts, and controversial or sensitive topics. It needs to avoid such cases without promoting hate speech or offending certain groups of people or their beliefs. This study uses a vector database-based Retrieval Augmented Generation (RAG) approach to enhance the accuracy and transparency of LLMs. Our question-answering system is called as "MufassirQAS". We created a vector database with several open-access books that include Turkish context. These are Turkish translations, and interpretations on Islam. We worked on creating system prompts with care, ensuring they provide instructions that prevent harmful, offensive, or disrespectful responses. We also tested the MufassirQAS and ChatGPT with sensitive questions. We got better performance with our system. Study and enhancements are still in progress. Results and future works are given.

翻译：在学习和理解宗教时，由于宗教教义与教理的复杂性和深度，存在诸多挑战。作为问答系统的聊天机器人有助于解决这些难题。基于大语言模型（LLM）的聊天机器人利用自然语言处理技术建立主题间的联系，并准确回答复杂问题。这些能力使其非常适合作为宗教启蒙领域的问答机器人。然而，LLM也容易生成错误信息（即幻觉现象）。聊天机器人的回复可能包含冒犯个人宗教信仰的内容，引发宗教间冲突，或涉及争议及敏感话题。需要避免此类现象，同时不助长仇恨言论或冒犯特定群体及其信仰。本研究采用基于向量数据库的检索增强生成（RAG）方法，以提升LLM的准确性和透明度。我们的问答系统名为"MufassirQAS"。我们利用多本包含土耳其语语境的开源书籍构建了向量数据库，这些书籍涉及伊斯兰教的土耳其语译本和注疏。我们精心设计了系统提示词，确保其指令能够阻止有害、冒犯或不尊重的回复。我们还使用敏感问题对MufassirQAS与ChatGPT进行了测试。我们的系统取得了更优性能。研究与改进仍在进行中。文中给出了结果及未来工作方向。