How to Tell When AI Is Lying to You

The most dangerous AI answer is the one that sounds right. A confident tone is not evidence, and the model has no idea when it is wrong.

Most people trust AI output by feel: fluent prose and a steady voice read as accuracy. That is exactly how fabricated citations, invented figures, and plausible-but-false claims slip into real work. The model does not flag its own uncertainty, so the burden of doubt falls on you.

This course builds that doubt into a repeatable practice. You learn the three categories AI distorts most, why surface specificity often masks a guess, and concrete checks like asking the same question twice in different framings to expose answers that shift under pressure. The goal is calibrated skepticism: knowing what to verify, what to discard, and what you can actually rely on.

Who it's for

Founders and operators: ship AI-assisted research, copy, and analysis without quietly inheriting its errors.

Writers and researchers: catch invented sources and false specifics before they land in a published piece.

Anyone using AI daily: build a fast verify-or-discard reflex instead of trusting the output by tone.