Learn how AI hallucination differs from a typical error—and how that affects what you should verify.
“Error” usually means something is simply wrong. “Hallucination” often implies it’s wrong in a specific way: the output presents unsupported claims as if they’re reliable.
The verification strategy changes. With hallucinations, you focus on evidence and sources. With smaller errors, you may focus on correction and context.
Use this quick guide:
Verify the claim with trusted references. Don’t just “trust the tone.”
Correct it by adding context, constraints, or the right details.
A risk signal helps you choose the right verification path.
Because it points you toward verification of evidence, not just correction of wording.
If sources are missing, treat the claim as something to verify before you rely on it.
Yes. Different parts can fail in different ways, so verification should be selective.
No. It helps you understand what kind of verification you need.