A general model is like a well-educated graduate. It knows a little about everything, but it does not know your products, your tone, or your way of doing things.
Fine-tuning is extra training on your own examples. You collect a few thousand cases of the task done well. The model studies them, and its internal settings shift slightly toward your way of doing it.
What You Gain
The gains are real. The model follows your format without long instructions. It handles your special cases. And often a small fine-tuned model can match a large general one on that task, at a fraction of the cost.
What It Costs
You need good examples, and those take work to collect. When a new base model arrives, you have to tune again. And the model becomes narrower: better at your task, worse at others.
When to Use It
Start simpler first. Write better instructions. Then give the model the right documents at the moment of the request. Fine-tune only when the task is stable and high in volume.
Do not train what you can simply tell. Fine-tune when telling is no longer enough.
The Business Engineer framework maps where each part of the AI stack sits and who holds the leverage. These pills are short lessons from the same library.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
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