The Calibration Gap: Your LLM Says 90% Confident but Is Right 60% of the Time
· 10 min read
Your language model tells you it is 93% sure that Geoffrey Hinton received the IEEE Frank Rosenblatt Award in 2010. The actual recipient was Michio Sugeno. This is not a hallucination in the traditional sense — the model generated a plausible-sounding answer and attached a high confidence score to it. The problem is that the confidence number itself is a lie.
This disconnect between stated confidence and actual accuracy is the calibration gap, and it is one of the most underestimated failure modes in production AI systems. Teams that build routing logic, escalation triggers, or user-facing confidence indicators on top of raw model confidence scores are building on sand.
