This case study explores Trigent Software's ambitious attempt to develop IRGPT, a specialized large language model for Ayurvedic medical consultations. The project represents an interesting intersection of traditional medicine and modern AI technology, while highlighting the real-world challenges of implementing LLMs in specialized domains with multilingual requirements. ## Project Overview and Goals The primary objective was to create a fine-tuned LLM that could facilitate Ayurvedic consultations in multiple languages, particularly focusing on South Indian languages including Kannada, Tamil, and Malayalam. The team aimed to build upon existing medical LLM foundations while incorporating specialized Ayurvedic knowledge and cultural context. This wasn't meant to replace practitioners but rather to serve as an AI assistant that could enhance their capabilities. The project had several key technical objectives: * Integration of ancient Ayurvedic concepts with modern medical terminology * Support for multilingual consultations with proper understanding of technical terms * Implementation of Ayurvedic diagnostic processes based on traditional concepts like doshas * Incorporation of cultural nuances and colloquial language * Generation of personalized treatment recommendations based on patient characteristics ## Technical Implementation The team adopted a comprehensive data-driven approach, assembling data from multiple sources: * PubMed dataset (273,000 rows) * Chatbot dataset (113,000 rows) * Ayurvedic books (approximately 2GB of text) * Synthetic data (35,000 rows) of patient interactions For the model architecture, they chose GPT-2 medium as their base model, working with: * 347 million parameters * Training infrastructure utilizing A100 GPUs * Standard GPT-2 architecture without significant modifications * Training run of approximately 590 batches until loss stabilization The implementation involved significant data preprocessing efforts: * Extensive deduplication and data cleaning * Translation and alignment of multilingual content * Structured organization of conversation pairs * Labeling and classification of medical queries and treatments * Integration of synthetic data generation pipeline ## Challenges and Learnings The project encountered several significant challenges that provide valuable insights for similar LLMOps initiatives: ### Data Quality and Availability The team discovered that while they had substantial raw data (potentially up to 1 million rows when considering all languages), the quality and validation of this data proved to be a major challenge. Translation accuracy and maintaining contextual relevance across languages proved particularly difficult. ### Cultural and Linguistic Nuances The attempt to incorporate multiple languages and cultural contexts revealed the limitations of current translation and language modeling approaches. The team found that standard translation methods were insufficient for maintaining the precise medical and cultural context required for Ayurvedic consultations. ### Technical Pivots Facing these challenges, the team made several strategic decisions: * Scaled back from the full multilingual implementation to focus on English * Simplified the architecture to focus on core functionality * Implemented a more targeted approach to data validation and processing ### Production Considerations While the team developed a working prototype, several production-related challenges emerged: * Need for more robust validation of medical advice * Challenges in maintaining consistency across different languages * Requirements for contextual awareness in responses * Integration complexities with existing medical systems ## Current Status and Future Plans The project is currently in a limited beta phase, with a functioning English-language prototype. The team has identified several areas for improvement: * Expansion of Ayurveda-specific training data * Enhanced capture of regional and cultural nuances * Improved context-aware response generation * Gradual reintroduction of additional languages ## Key Takeaways for LLMOps This case study offers several valuable insights for similar LLMOps projects: 1. Domain Specificity: Specialized domains like traditional medicine require carefully curated training data and domain expertise integration. 2. Multilingual Challenges: Supporting multiple languages in specialized domains is significantly more complex than general language translation. 3. Iterative Approach: Starting with a simplified scope and gradually expanding functionality can be more effective than attempting to implement all features simultaneously. 4. Data Quality: The importance of high-quality, validated training data cannot be overstated, particularly in specialized domains. 5. Cultural Context: LLMs need to account for cultural and regional variations, especially in healthcare applications. The project demonstrates both the potential and challenges of applying LLMs to specialized domains, particularly when attempting to bridge traditional knowledge with modern AI capabilities. While the full multilingual implementation remains a work in progress, the learnings from this project provide valuable insights for future similar endeavors in the LLMOps space.