The feasibility check compares a properly engineered prompt against a fine tune on your own evaluation set
If prompting wins, that is the answer, and you have spent a fraction of the budget to learn it
Custom models earn their place on narrow high-volume tasks, on domain language, and where data cannot leave
Coverage of edge cases, label consistency and the split strategy are what move the numbers
Most of the effort goes there, and the estimate says so rather than pretending training is the hard part
A thousand carefully labelled examples beat ten thousand sloppy ones
We build on permissively licensed base models unless you ask otherwise
Licence constraints get flagged before training rather than discovered at deployment
The evaluation harness comes with it, so you can re-run the comparison when a better general model ships
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Often not. We check by measuring, comparing a properly engineered prompt against a fine tune on your data. When prompting wins we tell you and stop there. It is a shorter engagement and a better outcome than a fine tune that gets abandoned.
Less than people expect for fine tuning a strong base model, often hundreds to a few thousand good examples. Quality and edge case coverage matter far more than raw volume.
You do. Weights, dataset, training code and evaluation harness. We use permissively licensed base models by default and raise any licence constraint before training starts.
It depends on model size and traffic, and we size against your targets during scoping. Distillation is frequently the difference between viable and not, so we look at it early rather than as an afterthought.
We re-run the comparison. If the new general model beats your fine tune, switching is the right call and we will say so. Building the evaluation harness at the start is what makes that decision easy later.
