Models are confident liars by default
Without explicit calibration, models report "I'm sure" for both correct and incorrect answers. This is dangerous downstream — humans treat AI confidence as signal. Calibration is the work of making reported confidence match actual accuracy.
Three calibration techniques
- Self-reported probability — "State your confidence (0–1) and your reasoning." Useful but coarse.
- Verbalized uncertainty — "State whether your answer is grounded in evidence, inferred, or unknown." Often more useful than numeric.
- Sample-vote signal — run N samples; if they disagree, that's the uncertainty signal. More reliable than self-reports.
Use confidence as a router
When model confidence is below threshold, route to: human review, a stronger model, additional retrieval, or a refusal. Confidence as a routing signal is more useful than confidence as a UI display.