How to audit AI-generated work: A 3-step checklist before hitting send

In my last post, we discussed why the most critical skill today is validation rather than prompt generation.

A question leaders and engineers often ask is: "What does a practical validation framework actually look like?"

Whether my team is reviewing AI-generated code, architecture proposals, or business analysis, we run every output through a 3-Layer Audit:

1. The Edge-Case Test: Did the model solve only the "happy path"?
2. Models excel at obvious scenarios but silently collapse on rate limits, malformed inputs, null states, or exceptions.
3. The Hallucination Anchor: Is every cited fact or dependency verifiable?
4. Never accept cited libraries, metrics, or policy claims without checking the source. Confident phrasing is not a proxy for factual truth.
5. The Context Gap: What organizational reality does the model not know?
6. AI doesn't know your company's legacy debt, compliance boundaries, budget constraints, or internal politics. That context must be injected by you.

Using AI without an audit layer is a liability. Using AI with a structured review process is an unfair advantage.

What’s your go-to check when reviewing AI-generated work?