This paper presents our system for Subtask 1: Islamic Inheritance Reasoning in the QIAS 2025 Shared Task, which evaluates large language models (LLMs) on ʿilm al-mawārīth (the Islamic science of inheritance) using a benchmark of Arabic multiple-choice questions (MCQs) derived from expert-reviewed fatwas. We explore static and dynamic few-shot prompting, retrieval-augmented generation (RAG) using a large fatwa corpus, and a progressive n-gram overlap retrieval method. The n-gram method is employed both to select the top five most similar MCQs for dynamic prompting and to retrieve the most relevant fatwa answers as additional context during inference. We evaluate both proprietary and open-source LLMs individually and in ensemble configurations. Results show that dynamic prompting and RAG consistently improve accuracy across models, with our best-performing model, Gemini, achieving 62.26% accuracy on the test set.