Fine-tuning is back for narrow tasks

For high-volume, tightly scoped tasks a small fine-tuned model now beats a large prompted one on cost by an order of magnitude, at comparable accuracy.

The blocker is no longer training cost. It is having a labelled set that reflects production traffic, which remains the least fashionable and most decisive asset a team can own.