Fine-tuning

A base model answers like a clever stranger. Show it a few thousand examples of your work, and it answers like your team.

Fine-tuning is extra training on examples you wrote: a few thousand replies, done your team’s way. The model keeps everything it knows. What moves is tone and habit. Same brain, different manners.

Manners, not facts

Fine-tuning changes how the model answers: your tone, your format, your categories. It files each question the way your team already does.

What does not change is what the model knows. Three thousand replies teach it how the bakery writes, nothing new about bread. Facts go in front of the model instead.

Like a new hire’s first week. Induction teaches the house style. The trade, they walked in with.
Fine-tunes wellTone, format, categories. Every reply in the house voice.
Does not fine-tune inFresh facts. This week’s prices, a closing date.

Hear the difference

One inbox, three questions, two versions of the same model. The fine-tuned one read three thousand of this bakery’s replies before its first shift.

Try it

The answers are prewritten and picked by your clicks. No model runs here. A real fine-tune trains this base model on your examples.

When to skip it

Most jobs need a better prompt. Write the perfect reply once, paste it in as an example, and the model copies it. You are finished.

Fine-tuning pays when the model still drifts by the hundredth message. It takes a few thousand examples, a checker, and a budget. Pay for drift you can measure.

Like paying for a tailored suit before you have tried the off-the-rack one.

Working on this?

Not sure whether your job needs a custom model? Tell me what it is. I read every one of these.