  Challenges exist in learning and understanding religions, such as the
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 for
enlightenment on religion as a question-answering chatbot. However, LLMs also
tend to generate false information, known as hallucination. Also, the chatbots'
responses can include content that insults personal religious beliefs,
interfaith conflicts, and controversial or sensitive topics. It must 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 "MufassirQAS". We created a database
consisting of several open-access books that include Turkish context. These
books contain Turkish translations and interpretations of Islam. This database
is utilized to answer religion-related questions and ensure our answers are
trustworthy. The relevant part of the dataset, which LLM also uses, is
presented along with the answer. We have put careful effort into creating
system prompts that give instructions to prevent harmful, offensive, or
disrespectful responses to respect people's values and provide reliable
results. The system answers and shares additional information, such as the page
number from the respective book and the articles referenced for obtaining the
information. MufassirQAS and ChatGPT are also tested with sensitive questions.
We got better performance with our system. Study and enhancements are still in
progress. Results and future works are given.
