  Large Language Models (LLMs) have demonstrated remarkable performance across
numerous natural language understanding use cases. However, this impressive
performance comes with inherent limitations, such as the tendency to perpetuate
stereotypical biases or fabricate non-existent facts. In the context of Islam
and its representation, accurate and factual representation of its beliefs and
teachings rooted in the Quran and Sunnah is key. This work focuses on the
challenge of building domain-specific LLMs faithful to the Islamic worldview
and proposes ways to build and evaluate such systems. Firstly, we define this
open-ended goal as a technical problem and propose various solutions.
Subsequently, we critically examine known challenges inherent to each approach
and highlight evaluation methodologies that can be used to assess such systems.
This work highlights the need for high-quality datasets, evaluations, and
interdisciplinary work blending machine learning with Islamic scholarship.
