Link: https://quranlm.com/

Mission & Core Tension

Welcome back to the QuranLM podcast, where we pursue the mission of "Bridging Divine Wisdom with Modern Intelligence." Our focus in this episode shifts to the critical tension between a probabilistic machine's confident tone and its capacity for complete factual error—especially when the subject is the Holy Quran.

We explicitly intend to probe the limits of Large Language Models (LLMs) and determine what it would take to make them truly trustworthy for sacred texts and Islamic scholarship.

Methodology: Stress Testing Modern Intelligence

We explore the text not by seeking structural patterns, but by stress-testing advanced AI models against the precision and complexity required by Divine Revelation. This process aims at uncovering the fragility of linguistic models and the ethical imperative of safety.

Each episode tackles a deep problem in AI application, analyzing diverse linguistic, legal, and theological challenges. Dive into discussions on profound concepts, such as:

Part 1: The Language & Logic Breakpoint (Where AI Breaks)

• Classical Arabic Nuance: We examine how Classical Arabic preserves morphological nuance that most models fail to capture. We detail how even small changes, like removing diacritics, fundamentally alter meaning and degrade performance.
• Stress Testing Inheritance Law: We push frontier LLMs to their limit using complex, conditional reasoning within Islamic inheritance law. This process exposes brittle chains of logic and the danger of authoritative-sounding hallucinations that cite verses that do not exist.

Part 2: The Ethical Imperative of Safety (How to Fix It)

• Ethical Alignment & Abstention: Using the Islam Trust benchmark, we explore the need for ethical alignment across Sunni schools of thought. We highlight abstention as a virtue—the model knowing when to say "I don't know"—and how current systems falter under ambiguity.
• Grounded Retrieval (RAG): We demonstrate why Retrieval-Augmented Generation (RAG) is essential. By chunking at the verse level and constraining answers to verified passages, RAG “chains the model to the truth,” sharply reducing fabrication and inventing doctrine.

Core Takeaway & Foundation

The core takeaway is simple and hard: LLMs must move from coherence to faithfulness. LLMs are pattern matchers, not authorities. They need curated data, ethical benchmarks, abstention policies, and grounded retrieval to serve scholarship instead of inventing doctrine.

We situate this work in centuries of human scholarship, drawing from historical context, manuscripts, and variant readings that continue to guide responsible system design for tools like QuranLM.

⚠️ Disclaimer: This discussion is not an endorsement of specific theological interpretations. We offer a critical, data-driven analysis for spiritual empowerment, safe research, and responsible technology development.

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