Verification + sourcing literacy: how to check what AI told you

• Check AI-generated claims by breaking them into specific facts, finding the official sources those facts should come from (vendor docs, government sites, published research), and seeing whether the sources actually say what the AI claimed.
• Sort information sources by trustworthiness: official documentation and original research beat blog posts and summaries; recognize when AI outputs might reflect commercial interests, training-data gaps, or made-up details that need independent confirmation.
• Trace AI answers back to their roots: pull out specific numbers or technical claims, search for them in known-good places, check multiple independent sources, and flag mismatches or invented citations before you use the information in production.
• Build a verification record for high-stakes AI outputs: write down the original claim, the authoritative sources you checked, what you found (supported, contradicted, or missing), and any corrections you made, so the next person can see where the information came from and whether it's solid.

Key concepts · tap to reveal

• “If an AI provides a citation or a reference, the claim is verified and trustworthy”

  AI systems can fabricate citations that look real but point to nonexistent documents, or they can cite real documents that do not actually support the claim. You must independently retrieve the cited source and confirm that the relevant passage says what the AI claims it says. Research on financial AI systems found that large language models invent citations as part of the same hallucination process that produces fabricated facts. Verification means checking the source yourself, not trusting that the AI checked it for you.