Fine-tuning is a process that involves training a base language model on new examples to slightly adjust its behavior, proving most useful for specific output formats, tone adjustments, and complex reasoning. The process, while theoretically straightforward, can be complex in practice due to the challenges of sourcing and formatting data, setting up training, and deploying updated models. The article illustrates this through two examples: first, manually fine-tuning a model to mimic a specific writing style, which involves converting varied data formats into a consistent training format, and second, using PromptLayer to streamline fine-tuning a workout generator model, which simplifies the pipeline and reduces the time and complexity involved. Open-source models like LLaMA and Mistral are commonly used due to their transparency, while closed-source models have varying degrees of accessibility for fine-tuning. Despite advancements, the fine-tuning process can be time-consuming and costly, with repeated iterations required to address data preparation and model training challenges, making tools like PromptLayer invaluable for efficiency.