Abstract:

The rapid adoption of large language models (LLMs) across industries has accelerated demand for domain-specific adaptations that deliver higher accuracy, stronger contextual understanding, and improved compliance compared to generic foundation models. Traditionally, fine-tuning, deploying, and governing these models requires complex multi-cloud infrastructure, specialised ML frameworks, and extensive data movement between systems—all of which introduce operational risk and slow enterprise adoption. Snowflake Cortex fundamentally changes this paradigm by enabling organisations to build, fine-tune, evaluate, and deploy domain-specific LLMs directly within the Snowflake Data Cloud, where their data already resides. This paper presents a comprehensive framework for developing domain-specialised LLMs entirely inside Snowflake using Cortex Fine-Tuning, Cortex Embeddings, Cortex Search, and Snowpark. We detail architectural patterns, governance boundaries, and MLOps workflows that allow enterprises to create compliant, secure, and scalable LLM systems without external model hosting. Through case studies in healthcare, financial services, and e-commerce, we demonstrate that Snowflake-native fine-tuning improves task accuracy by up to 30–50% while reducing infrastructure overhead, latency, and operational complexity. This research provides one of the first systematic analyses of Snowflake-native LLM development and offers practical guidance for organisations seeking to operationalise customised generative AI at enterprise scale.

Keywords:

Domain-Specific Large Language Models, Snowflake Cortex, Fine-Tuning, Retrieval-Augmented Generation (RAG); Enterprise Generative AI

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