calibrated-confidence

Confidence calibration prevents false certainty by making AI explicitly flag assumptions and verification steps before stating facts. It enforces a no-hedge rule unless genuine uncertainty is present, ensuring verifiability across coding and decision-making tasks.

Confidence calibration levels (HIGH, MEDIUM, LOW, GUESSING) dynamically drive response pace and detail. When confidence is low, the AI slows down, asks for clarifications, and provides 2-3 alternatives instead of offering a single potentially incorrect answer.

Calibrated confidence surfaces assumptions during debugging by applying explicit uncertainty flags. It adjusts depth and hedging based on confidence levels, requiring the AI to offer alternatives and verification steps rather than presenting unverified guesses as facts.

Yes, confidence calibration applies safely across coding, debugging, and decision-making tasks. It adjusts speed and detail based on defined confidence levels, ensuring outputs remain verifiable and preventing false certainty in any technical context.

Calibrated confidence reduces unnecessary hedging by enforcing a no-hedge rule unless genuine uncertainty is detected. It replaces vague hedging with explicit confidence levels and offers 2-3 targeted alternatives or questions when confidence is low.

False certainty occurs when AI lacks metacognition about its own knowledge limits. Confidence calibration fixes this by defining explicit confidence levels that adjust output speed, depth, and hedging, forcing the AI to surface assumptions and verification steps.