Dataset Shape Before Training

A dataset can be large and still weak if it does not actually express the behavior you want. Tone consistency, edge-case coverage, and prompt-response shape often matter more than volume alone.

For conversational adapters, the best training sets usually feel authored rather than scraped. They show what the model should do, not just what text happens to look similar.

Ruixen retains this as a foundational support note because many training failures start before optimization even begins.