Most GenAI demos optimize for fluency. Production systems need calibrated confidence: when the model should abstain, escalate, or ask for a narrower query.

A practical pattern is a daily calibration loop — small, timed tasks that force operators to estimate scale (latency budgets, corpus size, error cost) before tuning prompts.

MAGNITUDE RUN in the lab is one such loop: silhouette scaling makes relative error visceral, the same way eval dashboards should surface off-by-factor mistakes, not only accuracy percentages.