AI Judgment
Five lessons · verify, privacy, calibrated trust
This is the thinking layer underneath the tool layer, when is AI wrong, what are the risks, when should you push back?
Lessons
- 01 How AI Gets Things Wrong
Hallucination, confident tone, training cutoffs, and bias in training data.
- 02 A Practical Verification Framework
Tier 1 light review, Tier 2 spot-check claims, Tier 3 verify before use.
- 03 Data Privacy and What Not to Put in a Prompt
M365 Copilot vs public tools; PII, confidential, client, and legal content.
- 04 Responsible Use in High-Stakes Contexts
Client-facing, leadership, HR, attribution, review, and transparency.
- 05 Building Good AI Judgment Over Time
Over-trust vs under-trust; habits; MillerKnoll data and review-before-send.
Five lessons on predictable failure modes, verification, data privacy, high-stakes use, and building calibrated trust over time.
Begin Lesson 1The goal is confidence grounded in understanding, not assumption.
Lessons read in approximately 20–30 minutes each. Verification practice on your real work takes longer and is the point.
Pairs with Copilot Basics, Prompt Engineering, and Getting Started, complete those first if prompting and daily use are still new.