# From RAG to Agentic: Validating Islamic-Medicine Responses with LLM Agents

arXiv: https://arxiv.org/abs/2506.15911 · HTML: https://mlsanity.com/paper/2506.15911/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 0 of its 16 references that are in the map.
- First posted on arXiv 18 Jun 2025.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Built on by (most cited first, 3 of 3)

- [Assessing Large Language Models on Islamic Legal Reasoning: Evidence from Inheritance Law Evaluation](https://mlsanity.com/paper/2509.01081/) (2025)
- [QU-NLP at QIAS 2025 Shared Task: A Two-Phase LLM Fine-Tuning and Retrieval-Augmented Generation Approach for Islamic Inheritance Reasoning](https://mlsanity.com/paper/2508.15854/) (2025)
- [Can LLMs Write Faithfully? An Agent-Based Evaluation of LLM-generated Islamic Content](https://mlsanity.com/paper/2510.24438/) (2025)

## Abstract (arXiv)

Centuries-old Islamic medical texts like Avicenna's Canon of Medicine and the Prophetic Tibb-e-Nabawi encode a wealth of preventive care, nutrition, and holistic therapies, yet remain inaccessible to many and underutilized in modern AI systems. Existing language-model benchmarks focus narrowly on factual recall or user preference, leaving a gap in validating culturally grounded medical guidance at scale. We propose a unified evaluation pipeline, Tibbe-AG, that aligns 30 carefully curated Prophetic-medicine questions with human-verified remedies and compares three LLMs (LLaMA-3, Mistral-7B, Qwen2-7B) under three configurations: direct generation, retrieval-augmented generation, and a scientific self-critique filter. Each answer is then assessed by a secondary LLM serving as an agentic judge, yielding a single 3C3H quality score. Retrieval improves factual accuracy by 13%, while the agentic prompt adds another 10% improvement through deeper mechanistic insight and safety considerations. Our results demonstrate that blending classical Islamic texts with retrieval and self-evaluation enables reliable, culturally sensitive medical question-answering.