The problem
Prompting hits a ceiling. The model mishandles your forms, ignores house style, or fails on rare but expensive cases.
How Forklabs approaches it
We build a labelled dataset from production examples, train or adapt a suitable base, compare against RAG-only baselines, and only ship if the eval moves. Fine-tuning is a tool, not a default.
What done looks like
- A dataset you own, not a black-box vendor fit
- Before/after eval on held-out cases
- A rollback to the previous model version
