Development of Conversational Systems Based on RAG and LLM for Prophetic-Herbal Medicine

Abstract

Prophetic and herbal medicine represent rich, traditional knowledge which is often underrepresented in modern digital systems but with recent advancements in large language models (LLMs), it became possible to build intelligent systems capable of understanding and responding to user queries in this specialized domain. Retrieval Augmented Generation (RAG) frameworks have further enhanced these capabilities by grounding language models in external knowledge sources. This work presents the development of conversational systems focused on Prophetic and herbal medicine in both Arabic and English independently. The English systems include a fine-tuned Mistral-7B model trained on a domainspecific dataset,with two RAG pipelines one using Mistral and another using DeepSeek. The Arabic systems include two RAG implementations as well: one using the Allam model and another based on DeepSeek, both adapted to handle Arabic language queries with high contextual accuracy. These systems aim to facilitate knowledge access, learning, and interaction with culturally significant medical content through modern NLP architectures.