An AI answer can sound certain and still be wrong.
Fluent language is not evidence. NIST identifies confabulation as a generative AI risk; check the underlying source before relying on a claim.
Illustrative photograph · alerkiv / UnsplashGenerative AI can produce a convincing explanation without establishing that the explanation is true. NIST’s Generative AI Profile identifies confabulation as a risk: systems may present incorrect material confidently.
The practical distinction is between a useful starting point and a verified answer. A model can help organize a question, suggest a research path, or explain terminology. For a consequential factual claim, the next step is to inspect the underlying evidence.
Our reading checklist is simple: does the linked source exist, does it support this particular claim, and is it current enough for the question? A citation that merely discusses the same topic is not enough.
At ItsLit, AI assistance is disclosed. These launch explainers were prepared by an AI assistant using linked sources; they have not received independent human editorial review. We do not describe that process as a guarantee of accuracy.
The evidence desk
Sources
What remains uncertain
No checking process eliminates every error. Source quality, context, and changes over time still matter.
Corrections
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