What it means
A general model already knows a great deal. Fine-tuning adjusts it with hundreds or thousands of your own examples, such as correctly classified tickets or replies in your house style, so it performs that specific task more consistently.
Fine-tuning teaches behavior and format well. It is not a good way to teach facts that change, such as prices or policies. For those, retrieval-augmented generation is usually better.
Why it matters for a business
Many teams reach for fine-tuning too early. Good prompts and retrieval often solve the problem at lower cost. Fine-tuning pays off when you have many good examples, a stable task and high volume, where a smaller tuned model can replace a larger, more expensive one.
A business example
Things to watch
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You need many good, consistent examples.
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Fine-tuned models need retesting when the base model changes.
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Do not use it to teach facts that change.
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Keep a held-back test set to prove the tuned model is actually better.