When does fine-tuning make sense over prompting? We explore LoRA, QLoRA, and full fine-tuning with real-world costs, datasets, and evaluation metrics.
Fine-tuning language models allows you to adapt a foundational model to a specific domain or task, achieving results superior to prompting for cases with specialized terminology or highly defined output formats. Techniques like LoRA and QLoRA have dramatically reduced training costs, making fine-tuning viable even for startups. In this guide, we share our methodology: when to choose fine-tuning versus prompting, how to prepare quality datasets, which hyperparameters to adjust, and how to evaluate results with objective metrics.