
AI reliability
OCR Can Read Every Number and Still Put It in the Wrong Row
Check extracted tables by row, column, unit and heading rather than assuming accurate character recognition means the structure is correct.

Check extracted tables by row, column, unit and heading rather than assuming accurate character recognition means the structure is correct.

Use a simple confusion-matrix example to see why a high accuracy score can hide the AI errors that matter most for the task.

Distinguish a model’s confidence from demonstrated reliability, using calibration checks on representative data before treating a score as a probability.

Design an AI workflow that can leave a question unresolved, request evidence or escalate instead of rewarding a fluent answer to every input.