The Impact of Generative AI on Islamic Studies: Case Analysis of "Digital Muhammad ibn Ismail Al-Bukhari"

The emergence of large language models (LLMs) such as ChatGPT, LLaMA, Gemini, and Claude has transformed natural language processing (NLP) tasks by demonstrating remarkable capabilities in generating fluent and contextually appropriate responses. This paper examines the current state of LLMs, their applications, inherent challenges, and potential future directions necessitating multidisciplinary collaboration. A key focus is the application of generative AI in Islamic studies, particularly in managing sensitive content such as the Ahadith (corpus of sayings, actions, and approvals attributed to the Prophet Muḥammad). We detail the customization and refinement of the AI model, "Digital Muḥammad ibn Ismail Al-Bukhari," designed to provide accurate responses based on the Sahih Al-Bukhari collection. Our methodology includes rigorous dataset curation, preprocessing, model customization, and evaluation to ensure the model’s reliability. Strategies to mitigate hallucinations involve implementing context-aware constraints, regular audits, and continuous feedback loops to maintain adherence to authoritative texts and correct biases. Findings indicate a significant reduction in hallucinations, though challenges such as residual biases and handling ambiguous queries persist. This research underscores the importance of recognizing LLMs’ limitations and highlights the need for collaborative efforts in fine-tuning these models with authoritative texts. It offers a framework for the cautious application of generative AI in Islamic studies, emphasizing continuous improvements to enhance AI reliability.