Protocol
Keep a list of high-confidence AI failures
The errors worth memorizing are the ones the model did not know were errors.
When it fits
- You are calibrating an AI assistant and ordinary error counts hide the most dangerous mistakes.
When to avoid it
- Do not infer a general failure mode from one example without additional evidence.
Why it matters
Maintain a compact failure set of cases where the AI was wrong while expressing high confidence or presenting unusually persuasive evidence. Tag the task shape and likely failure mode. Use these cases in future evals, user warnings or routing rules.
Steps
- The system's most misleading failures are reusable test cases rather than anecdotes lost in chat history.
An example
A coding agent confidently edits the wrong configuration scope because two environments use near-identical names; keep that case in the eval set.
Check your result
The system's most misleading failures are reusable test cases rather than anecdotes lost in chat history.
Keep this limit in mind
- Do not infer a general failure mode from one example without additional evidence.
Evidence and sources
Supports
An AAAI 2026 experiment found well-calibrated AI confidence improved participant decision accuracy more than miscalibrated confidence, while miscalibrated cues increased reliance-related errors.
The task involved logic puzzles and controlled confidence manipulations.
Too Sure for Our Own Good: A User Study on AI Confidence and Human Reliance · Results