Fine-tuning

Fine-tuning Explained

Fine-tuning trains a model on your dataset to learn your specific style, terminology, or task format. This can improve quality and reduce token usage by eliminating lengthy prompts. However, fine-tuning has upfront costs (training) and ongoing costs (inference on fine-tuned models). Consider fine-tuning when you have consistent, high-volume use cases where prompt engineering isn't sufficient.