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AI Hallucinations and Output Verification
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AI Hallucinations and Output Verification

Check AI-generated content before you use or share it: verify claims, sources, summaries and numbers, then decide.
Last updated 11/2026
English

What you'll learn

  • Explain what an AI hallucination is and why AI tools state errors with confidence
  • Spot the claims in AI output that need checking: figures, quotes, citations, dates and policy statements
  • Decide which claims to verify first, based on the harm a wrong claim could do
  • Apply a five-step workflow: extract claims, assess risk, locate evidence, compare and check, document a decision
  • Check whether a cited source exists, is current and relevant, and actually supports the claim made
  • Compare AI summaries with their source documents and recompute AI-generated numbers yourself
  • Write prompts that set source boundaries and ask for traceable evidence and stated uncertainty
  • Keep a verification record and decide whether to use, revise, withhold or escalate an AI output

Course content

3 sections • 8 lectures • 1h 13m total length
  • A Polished Answer Can Still Be Wrong9:55

    Meet the course case: an AI-drafted vendor report that sounds certain. Learn what an AI hallucination is, why AI tools state errors with confidence, and how to tell a verified fact from a fabrication, an omission, an outdated claim and an answer to the wrong question.

  • Find the Claims That Matter8:54

    Break an AI output into atomic, checkable claims, sort them by type, and watch for the flags that signal a claim needs evidence. Then rank claims by the harm a wrong one could do and choose your first three checks. Resource: Tessaline evidence pack.

Requirements

  • No coding, technical or AI background is needed
  • Some experience with a generative AI assistant at work is helpful, but not required
  • No software to install. Downloadable worksheets open in Word, Excel or any PDF reader

Description

This course contains the use of artificial intelligence.

An AI assistant can draft a polished report in an hour. It will cite sources, quote experts and give exact figures, and some of it may be wrong. AI tools state errors with the same confidence as facts, and fluent writing makes those errors easy to miss. This course gives you a repeatable method for checking AI-generated content before you use it, share it or base a decision on it.

You'll follow one realistic case from start to finish: the operations team at a fictional delivery company receives an AI-drafted vendor recommendation report the day before a leadership meeting. The report sounds certain. As you work through it, you'll uncover a misquoted analyst, a statistic its own source doesn't support, an outdated claim, a missing policy exception and a cost total that leaves out a fee, along with claims that check out and one that simply can't be confirmed yet.

What you'll learn:

  • How to break an AI output into checkable claims and decide which to verify first.

  • A five-step verification workflow: extract claims, assess risk, locate evidence, compare and check, and document a decision.

  • How to check whether a cited source exists, is current and relevant, and actually supports the claim.

  • How to compare AI summaries with their source documents and recompute numbers yourself.

  • How to write prompts that set source boundaries and ask for traceable evidence and stated uncertainty.

  • What AI-side checks, such as web search, self-checks and a second AI's agreement, can and can't prove.

  • How to keep a verification record and decide whether to use, revise, withhold or escalate an output.

The course is presentation-based, with worked examples and pause-and-decide exercises throughout. No software, coding or technical background is needed. Downloadable resources include the case evidence pack, a verification record template, a source-bounded prompt template and a one-page output verification checklist.

Whether you draft with AI yourself or review work that others have drafted with it, you'll finish with a method you can use on any AI output you rely on.

Who this course is for:

  • Business professionals who use AI tools to draft, summarize, research or support decisions
  • Team leaders and managers who review AI-assisted work before it's shared or acted on
  • IT support staff and analysts who help colleagues use AI tools responsibly
  • Anyone who wants a repeatable way to check AI output before relying on it