Fundamentals

Fine-Tuning

Fine-tuning involves taking a pretrained model and continuing training on your own dataset. This allows you to adapt a general-purpose model to specific tasks, domains, or writing styles. Fine-tuning can improve performance dramatically compared to prompt engineering alone. However, it requires preparation: you need training data, computational resources, and time. Fine-tuning works best when you have thousands of quality examples. It's also risky—bad fine-tuning data can degrade the model. Most developers start with prompt engineering and only fine-tune when necessary.