AI text can sound completely authoritative while being wrong. Here’s how to actually check it, framed around the questions people usually get stuck on.
Do I need to check everything?
No. General explanations of established concepts are lower-risk. Specific numbers, dates, quotes, and citations are exactly where hallucinations concentrate. Spend your effort there.
What about citations specifically?
Never trust one without checking it exists. Models can generate a complete, plausible-looking citation, author, journal, year, describing a paper that isn’t real. Search for the actual source and confirm it says what’s claimed.
Does search grounding solve this?
It helps. Models with live web search, like Gemini’s Search integration, are meaningfully more reliable for current facts, but still worth checking, since sources themselves can be misread or misapplied.
What’s the single best habit here?
Cross-check anything surprising or unusually convenient against an independent source before using it. Confirmation bias applies to AI output the same way it applies to everything else you read, and checking the date matters too, since a model can state something that was true at its training cutoff but has since changed.
See the International Fact-Checking Network for general verification standards.
