Calibrating judgment about executor reliability

Module 2 · Lesson 4 of 5

One of the most practically difficult aspects of directing work — particularly with AI — is knowing when to accept an output and when to push back. The stakes matter. If you over-trust, you let wrong work through and it becomes someone else's problem. If you under-trust, you rewrite everything, which defeats the purpose of direction and wastes the executor's capabilities. The right level of trust is not constant. It depends on domain, context, the stakes of the work, and your own calibration of the executor's reliability.

Getting this calibration wrong is one of the main reasons people fail at directing AI effectively. They either become hands-off and get surprised by errors, or they become quality-control gatekeepers who rewrite everything, which is expensive and doesn't scale.