Fact-Checking AI: How to Spot Errors, Reduce Hallucinations, and Trust Your Output

Course details

AI tools are confident, fluent and often wrong. The real risk isn’t that AI deliberately lies; it’s that it can’t tell when it’s inventing information. That makes unchecked outputs risky for anyone whose name is attached to the work. In this course, you’ll learn why AI outputs fail, how to prompt in ways that reduce the risk of errors, and how to verify AI‑generated content before you share it. Because the cost of getting it wrong to your reputation, your organization, and the broader idea of truth is higher than most people realize until it’s too late.

Learning objectives
Identify why AI outputs fail and recognize the warning signs of fabricated or unreliable content.
Distinguish between different types of AI errors, from obvious hallucinations to subtle inaccuracies.
Apply prompting techniques that reduce error rates and encourage transparency about uncertainty.
Evaluate AI outputs systematically before sharing or publishing them.
Verify facts, sources, and figures using efficient, repeatable method.
Use AI tools to critique and improve other AI‑generated work.
Establish a personal verification workflow that balances speed with accountability.

Concepts

Introduction

Fact-Checking AI
Why you need to add fact-checking to your skillset

Why AI Verification Matters

AI mistakes have your name on them
Resisting the temptation to skip AI verification

How AI Errors Happen

Why AI fabricates information without lying
Why polished AI output is harder to verify

Spotting AI Errors and Hallucinations

Common AI errors you need to be aware of
Red flags that signal unreliable AI output
How to catch AI reasoning errors and logical fallacies

Reducing and Catching AI Hallucinations

Prompting techniques that reduce AI hallucinations
A step-by-step AI verification workflow

Checking the Actions of Agents

When AI errors go beyond words to actions

Summary and Next Steps

Making fact-checking a habit