This paper introduces a comprehensive system designed to address two natural language processing tasks: Passage Retrieval (Task A) and Reading Comprehension (Task B), applied to datasets related to the Holy Qur’an. Task A was treated as a measurement of a textual similarity problem where the system leverages OpenAI’s “text-embedding-ada-002” embedding model to transform textual content into numerical representations, with cosine similarity serving as the proximity metric. Task B focuses on the extraction of answers from Qur’anic passages, employing the Generative Pre-trained Transformer-4 (GPT-4) language model. In Task A, the system is evaluated using the Mean Average Precision (MAP) metric, achieving MAP scores of 0.109438 and 0.06426543057 on the development and test datasets with an optimal similarity threshold set at 0.85. Task B evaluation employs partial Average Precision (pAP), where our system surpasses a baseline whole-passage retriever with pAP scores of 0.470 and 0.5393130538 on the development and test datasets, respectively.