Unlocking Business Value with Fine-Tuning

This document explains how fine-tuning large language models helps enterprises unlock higher accuracy, efficiency, and domain relevance from generative AI. It positions fine-tuning as a practical middle ground between prompt engineering and full model training, especially when combined with retrieval-augmented generation (RAG). By tailoring models to business-specific data, organizations can reduce costs, improve latency, and achieve consistent, high-quality outputs aligned with operational and brand needs.

• Fine-tuning improves accuracy, relevance, and efficiency by adapting pre-trained models to domain-specific tasks, reducing prompt length, token usage, and response latency.
• A hybrid approach combining prompt engineering, RAG, and fine-tuning delivers optimal performance by grounding responses in real-time data while embedding domain expertise.
• Successful fine-tuning depends on high-quality data, clear evaluation metrics, careful model selection, and iterative experimentation to avoid overfitting and control costs.

The guide also outlines the end-to-end fine-tuning journey—from data preparation and model selection to training, deployment, and monitoring—along with techniques such as supervised fine-tuning, direct preference optimization, reinforcement fine-tuning, and model distillation. Real-world use cases across healthcare, finance, legal, and agriculture illustrate tangible business impact. Microsoft Foundry is presented as an enterprise-ready platform that simplifies fine-tuning with built-in security, evaluation, and governance, enabling organizations to scale specialized AI solutions responsibly and efficiently.