Five-Minute Routine to Fact-Check AI Responses Effectively

AI language models generate fluent, confident text without any internal mechanism to verify whether claims are true, making human fact-checking essential. A practical approach focuses on three high-error categories: citations, specific attributions, and numbers, as these account for the most consequential mistakes. Every cited source should be searched by exact title, since fabricated references often mimic real ones closely enough to fool a casual reader. Proper nouns paired with specific facts — such as job titles, founding years, or publication credits — should be verified independently, as AI models produce plausible-sounding attributions with high confidence. Spot-checking at least one piece of arithmetic and asking what evidence would genuinely support the central claim rounds out a routine that takes roughly five minutes and catches the errors most likely to cause harm.