Fine-tuning with OpenAI

We start with a base model. Think of it as the generalist—she knows a lot, but not in the way we need. Fine-tuning is the trick for nudging her toward our use case. We don’t rebuild her; we just retrain parts so she pays more attention to what matters.

Training data

The key is the examples we give her. If our training data is messy, she learns the mess. If it’s clear, she learns the patterns fast. We don’t need millions of lines, but we do need the right ones. It’s like teaching a friend how we like our coffee: better to show ten good orders than a thousand confusing ones.

Process

OpenAI lets us upload a set of input-output pairs. She sees a prompt and the desired answer. Over time, she adjusts her internal knobs so she starts spitting out the right format. We don’t see the knobs—thankfully. We just get back a version of her tuned for our jobs. It’s closer to editing a style guide than building a dictionary.

Why do it

Default models are fine for broad strokes. But if we need consistent tone, legal phrasing, or domain-specific replies, fine-tuning saves us from endless prompt engineering. She starts answering in our style without the extra coaxing. Less fiddling, more results.

Limits

She won’t learn things outside her scope. If our dataset is too small or contradictory, she gets confused. Also, fine-tuning is slower and costs money. So we use it when the gain outweighs the hassle. Otherwise, we just stick with clever prompting.

We like that fine-tuning feels less like magic and more like gardening. Feed her the right data, prune the noise, and she grows into the helper we need. Which makes us wonder—maybe the real skill isn’t coding her, but curating what she sees.