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 public LLMs with internal data to address both challenges. Drawing on established techniques from parameter-efficient fine-tuning, retrieval-augmented generation, and preference alignment, we outline practical approaches suited to organizations of varying technical maturity.