By the end of this station, you can:
- Explain what an AI hallucination is and why it happens
- Recognize that a confident tone is not evidence of accuracy
- Use a quick routine to catch made-up facts and citations
Fluent is not the same as true
Ask a chatbot a question and you usually get a smooth, well-organized answer. Most of the time that answer is useful. But sometimes part of it is simply made up — a statistic that doesn't exist, a book that was never written, or a quote nobody ever said.
This is called a hallucination. The tool isn't lying on purpose, and it isn't "seeing things." It is doing exactly what it was built to do: produce text that looks like a likely answer. Looking likely and being correct are two different things.
Key terms
- Hallucination
- When an AI system produces information that sounds plausible but is false, unsupported, or invented — such as fake facts, sources or quotes.
- Fabricated citation
- A reference to a book, article, study or web page that looks real but does not exist, or that does not say what the AI claims it says.
Why it happens
Large language models generate text by predicting what is likely to come next, one small piece at a time. They learned patterns from huge amounts of writing, so they are very good at producing the shape of a good answer — the right tone, structure and vocabulary.
But the model does not look facts up in a verified database by default. If it hasn't learned a reliable pattern for your exact question, it can still produce something that fits the pattern of an answer. A citation, for example, has a predictable shape: author, year, title, journal. The model can generate that shape even when no such paper exists.
Some tools can search the web or read documents you give them, which can help. Even then, they can misread a source or mix details from different places, so checking still matters.
Knowledge check 1
+10 XP on first tryInteractive lab
Hallucination Hunt
A fictional chatbot answered five questions — and every answer sounds very sure of itself. Trust it or flag it?
Answered 0 of 5
A 60-second hallucination check
Spot the checkable claims
Names, dates, numbers, quotes and sources are the parts most worth checking — and the parts most likely to be invented.
Search for the source itself
Look up the exact title, author or quote. If a paper or book can't be found in a library database or on the publisher's site, don't use it.
Read what the source actually says
Even real sources get misquoted. Make sure the source supports the specific claim the AI attached to it.
Cross-check with an independent source
A second trustworthy source that agrees is much stronger evidence than asking the same chatbot "Are you sure?"
Myth vs. fact
MythIf I ask the AI whether its answer is correct, it will tell me.
FactA model can confidently "confirm" an answer that is wrong, or change a right answer just because you pushed back. Check against outside sources instead.
MythHallucinations only happen with obscure topics.
FactThey are more common on niche or very recent topics, but they can show up anywhere — especially in details like dates, quotes and citations.
Knowledge check 2
+10 XP on first tryLearn moreCan hallucinations be fixed completely?
Developers use several techniques to reduce hallucinations, such as connecting models to search tools or trusted documents and training them to say "I'm not sure." These help, but no current method removes the problem entirely.
That's why the responsible habit isn't "find a tool that never hallucinates." It's "treat AI output as a draft and verify anything that matters."
Finish this station
Answer every knowledge check (right or wrong) to unlock completion.
- Knowledge check 1 — not answered yet
- Knowledge check 2 — not answered yet
0/2 checks answered