AI fact-checking for generated content

AI-generated content is now cheap enough that the question "can the model write this?" has been replaced by "can we trust what it wrote?". Hallucination — confidently inserting facts that don't exist — is the failure mode that blocks production deployment for any application where the output is read by a human who will act on it.

There is no single fact-check method that catches every hallucination. Modern fact-checking systems stack three complementary techniques that each fail in different ways: mechanical entity extraction (cheap, catches invented proper nouns and numbers), claim-level verification (LLM-call, catches invented relationships), and source corroboration (free signal, weights single-source claims as lower-confidence). Combined into a single confidence score, the stack catches the vast majority of hallucinations that single-method systems miss.

Notifire publishes its fact-check confidence score on every article and documents the methodology openly. This hub aggregates Notifire's coverage of the field — model-grading techniques, eval-framework releases, AI-content provenance standards, and the policy debates around AI-generated journalism — alongside a worked example of how the stack runs in production.