Part 1: Why Train an LLM on Hindu Scriptures? The Vision, Challenges, and the Role of Pristine Data

Introduction

Hinduism, one of the world’s oldest traditions, boasts a vast and profound literary heritage. The Vedas, Upanishads, Puranas, Epics, and other scriptures hold timeless wisdom on philosophy, ethics, spirituality, and human nature. As artificial intelligence advances, there is an unprecedented opportunity to digitize, structure, and make this knowledge universally accessible.

Imagine an AI that can:

• Answer deep philosophical questions from the Upanishads or Bhagavad Gita.
• Explain the intricate symbolism of Vedic rituals.
• Generate engaging, modern adaptations of Puranic stories.
• Assist scholars and students in navigating Hinduism’s vast corpus of texts.

This is where Large Language Models (LLMs) come into play. By training an LLM on Hindu scriptures, we can create a specialized AI capable of preserving, exploring, and disseminating this wisdom in an intuitive way. But to achieve this, we must first understand the dataset, the challenges, and—most importantly—the critical role of pristine data.

The Importance of Pristine Data for LLM Training

The success of any LLM depends on the quality of its training data. Inaccurate, biased, or corrupted data leads to hallucinations, distortions, or misinterpretations of the original knowledge.

Hindu scriptures, passed down for thousands of years, have been carefully preserved through oral and written traditions, making them a gold standard for pristine data. They exhibit:

1. Consistency: Sanskrit scriptures follow strict grammatical rules, ensuring precision.
2. Semantic Depth: Concepts like Dharma, Atman, Karma require nuanced understanding, best captured in their original context.
3. Minimal Corruption: Unlike folklore that evolves over time, Vedic texts have remained largely intact due to rigorous transmission practices.

By ensuring high-quality digitization, accurate transliteration, and faithful translations, we can train an LLM that respects and retains the purity of the original texts rather than producing distorted interpretations.

The Dataset: A Treasure Trove of Knowledge

Hindu scriptures form an expansive dataset spanning multiple genres:

1. Vedas – The oldest texts containing hymns, rituals, and spiritual insights.
2. Upanishads – Philosophical treatises exploring metaphysics and the self.
3. Puranas – Mythological and historical narratives about gods, sages, and cosmic cycles.
4. Epics – The Ramayana and Mahabharata, which blend storytelling with ethical and spiritual lessons.
5. Dharma Shastras – Texts outlining law, ethics, and social conduct.
6. Agamas & Tantras – Ritualistic and esoteric scriptures.

Dataset Size: Approximately 5–10 million words (~50–100 MB of text). While this may seem small compared to modern LLM datasets trained on billions of words, its semantic density makes it uniquely valuable.

Challenges in Training an LLM on Hindu Scriptures

Developing a high-quality LLM for Hindu scriptures presents unique challenges:

1. Language Complexity – Many scriptures are in Sanskrit, while others exist in Hindi, Tamil, and English translations. Ensuring contextually faithful translations is crucial.
2. Philosophical Depth – Abstract concepts like Brahman, Maya, Moksha require deep contextual understanding rather than literal translations.
3. Structural Complexity – Vedic hymns, meter-based compositions, and structured dialogues must be preserved for accurate responses.
4. Dataset Size – Compared to the internet-scale corpora used for models like GPT, Hindu scriptures are relatively small. However, their pristine quality and depth compensate for the lack of sheer volume.

Overcoming these challenges requires a specialized architecture. Enter the Mixture of Experts (MoE) approach.

Mixture of Experts (MoE): The Ideal Model for Sacred Texts

A Mixture of Experts (MoE) model works like a team of specialists:

• One expert focuses on Vedic hymns.
• Another specializes in Upanishadic philosophy.
• A third understands Puranic storytelling.

When a query is posed, the model intelligently selects the most relevant expert to respond.

How MoE Benefits Hindu Scripture Training:

• Domain Specialization – Different texts require different levels of expertise. MoE allows focused training on each category.
• Efficient Computation – Instead of activating all parameters, MoE selectively engages the most relevant ones, reducing computational costs.
• Context Retention – Ensures responses remain accurate and true to the original text rather than blending diverse sources improperly.

What’s Next?

In Part 2, we’ll explore the technical aspects of building this LLM:

• Designing the MoE architecture for Hindu scriptures.
• Defining the ideal number of parameters and layers.
• Strategies for fine-tuning and augmenting the dataset.
• Practical steps to bring this AI-powered Hindu knowledge assistant to life.

Stay tuned!