
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is, why AI tools state errors with confidence, and how to tell a verified fact from a fabrication, an omission, an outdated claim and an answer to the wrong question.
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.
Learn the five-step workflow: extract claims, assess risk, locate evidence, compare and check, and document a decision. See why asking the same AI 'are you sure?' isn't verification, what makes evidence authoritative, and how to keep a verification record. Resource: verification record template.
Climb the source ladder: does the source exist, is it current and relevant, and does it actually support the claim? Check quotations word for word, spot circular repetition, and learn what to do when a source can't be found.
Compare an AI summary with its source clause by clause, trace and recompute every number, and separate evidence from assumptions and recommendations. See how a recommendation changes when the evidence is corrected.
Rewrite an open-ended prompt with a defined task, supplied sources, a required structure, clear boundaries, and a source and status for every claim. Ask for a claim table that keeps known, assumed and missing apart. Resource: source-bounded prompt template.
Learn what retrieval, web browsing, self-checks, a second AI's agreement and confidence statements can and can't prove. Map who must review what, what data may go into AI tools, how to note AI use, and when to escalate.
Apply the whole method to the full case: prioritize checks, complete the verification record, label what's unresolved, and choose a justified use, revise, withhold or escalate decision. Resources: Tessaline evidence pack and output verification checklist.
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.