Evaluating Voice After Training

The easiest trap after training is deciding too early that the adapter works because one or two answers feel right. Voice needs repetition, variation, and pressure before it can be trusted.

A good evaluation set should probe short replies, longer replies, disagreement, formatting requests, and whatever edge cases would embarrass the model if it failed in public.

Ruixen keeps this note close because share-readiness is really evaluation-readiness in disguise.