Why do you place so much emphasis on calibration over raw accuracy?

Calibration means that when the model says 80 percent confidence, it is actually right 80 percent of the time. This matters enormously for software because thresholding is how you control behavior. If the model is miscalibrated, your threshold is meaningless. You want similar inputs to produce similar outputs. We test this by putting unique IDs called nonces into prompts that are semantically identical and checking that the outputs are consistent. This is robustness, and it is where AI most frequently burns developers today. They think the model should be smart enough to automate a task, there is economic incentive to automate it, yet they cannot trust the output because the distribution is too jagged.