This paper focuses on the development and systematic comparison of Retrieval-Augmented Generation (RAG) systems, retrieval-only systems and LLM models all trained on ancient Sanskrit Scriptures. This was done in order to analyse whether RAG systems improved faithfulness in answers to reflective questions, by storing two pertinent Sanskrit scriptures: the Itihasa (including the Mahabharata and Ramayana) and the Bhagavad Gita in a FAISS index, I developed the following: a basic retrieval system from the FAISS index, a prebuilt LLM model (Qwen 2.5-3B-Instruct), an RAG system with the LLM model Qwen 2.5-3B-Instruct and an RAG system with Gemini 2.5 Flash. After development, I evaluated the four models on a list of twenty questions pertaining to philosophy, interpersonal and intrapersonal understanding, and emotional well-being. I ranked each answer on a scale from 1 to 5 on relevance, helpfulness, clarity and faithfulness. All retrieval and RAG models scored a perfect 5 in the ‘faithfulness’ metric in contrast to the base LLM model, which scored a 4.3. Moreover, I discovered that the use of a weaker LLM model in an RAG system can lead to worse results in the ‘helpfulness’ and ‘clarity’ metrics when compared to a regular LLM model when the retrieved verses are low. Through the methods and results of my research, I showed that RAG systems are necessary to provide specific and faithful answers from ancient Sanskrit philosophy.