Fine-tuning large language models (LLMs) is crucial for adapting general-purpose models to meet enterprise-specific requirements such as precision, compliance, and reliable outputs. Unlike pre-training, which equips models with broad language understanding, fine-tuning updates a pre-trained model's weights using specialized datasets to align it with niche domains such as healthcare, finance, or legal sectors. There are several approaches to fine-tuning, including full fine-tuning and parameter-efficient methods like LoRA, which balance computational cost and effectiveness. Fine-tuned models significantly improve accuracy, reduce error rates, and provide consistent structured outputs, making them indispensable in environments that require strict adherence to domain-specific terminology or regulatory standards. Fireworks AI offers a robust platform for fine-tuning, providing tools for efficient training, deployment, and continuous evaluation, thus helping organizations transition from experimental models to scalable, enterprise-grade AI systems.