Fine-Tuning, explained
Fine-tuning is further training a model on your own examples so it learns a specific style or task, changing how the model behaves rather than just what you tell it in a prompt.
Out of the box, a model is a generalist. Fine-tuning takes that model and trains it further on a set of your own examples, so it gets better at a particular style, format, or task by default, without you having to spell it out every time.
It is often confused with two simpler approaches. Prompting changes the instruction; RAG feeds the model relevant documents; fine-tuning actually changes the model's behavior. For most people and most tasks, good prompting and RAG get you there without the cost and effort of fine-tuning.
Fine-tuning is worth it mainly when you need a consistent, specialized behavior at scale and prompting alone is not enough. For everyday use, it is usually the last tool to reach for, not the first.
Go deeper
Wield's AI Foundations track covers this hands-on, in plain English, with real examples and a copy-paste prompt to try it yourself.
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