Fine-Tuning Public LLMs with Internal Data
Large Language Models (LLMs) have transformed enterprise AI, but deploying them against proprietary internal data introduces two critical challenges: hallucination — where the model generates plausible but incorrect information — and poor contextualization, where responses lack the domain specificity required for business use. This whitepaper presents a structured framework for fine-tuning