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What "Hallucination" Actually Means, In Plain Terms
By Nigel Guy · 2 min read
"Hallucination" sounds dramatic — like the tool is malfunctioning or seeing
things. What it actually means is much more mundane and, in a way, more
important to understand correctly: the model generated something fluent and
plausible-sounding that isn't true, with no internal alarm bell going off,
because it doesn't have one.
The rule: a hallucination isn't a glitch you'll notice — it's a normal,
expected output that happens to be wrong, which is exactly why catching it
is your job, not the tool's.
The mechanism for understanding it
- Know it's not rare or exotic. It's a routine feature of how these
models generate text, more likely in certain situations (obscure facts,
very specific numbers, citations) than others, but never fully absent.
- Notice the situations where it's more likely. Precise statistics,
specific citations or sources, niche facts with little public
information, and anything asked with a leading or narrow framing that
invites a confident-sounding guess.
- Treat fluency as irrelevant to truth, deliberately. A hallucinated
fact reads exactly as smoothly as a correct one — see "Why 'It Sounded
Right' Is The Most Dangerous Sentence" for the full version of this.
- Verify independently for anything that matters, especially specific
numbers, quotes, or citations — these are exactly where hallucination
shows up most.
What to skip
Skip treating a caught hallucination as evidence the tool is generally
unreliable — it's evidence that this particular check worked. Skip assuming
a more capable or newer model has "solved" this; it's reduced, not
eliminated, across every model generation so far.
Guardrails
- The word matters less than the habit — whatever term is in fashion,
the underlying behaviour (fluent, wrong, undetectable by tone alone) is
what to build your verification habits around.
- Citations and specific sources deserve particular scrutiny — a
fabricated-but-plausible citation is one of the more common and more
costly forms this takes.
- This is a property of the technology, not a flaw specific to one product —
the same caution applies regardless of which AI tool produced the answer.
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