Every AI-generated article contains a predictable distribution of error types: confident-sounding statistics without sources, plausible-but-wrong dates, real names attached to quotes they never said, and invented institutional details. An effective audit framework addresses each error type systematically rather than reading the article as a human would.
Step 1: Claim Inventory
Read the article and underline every factual claim — statistics, dates, named institutions, direct quotes, attributed opinions, cause-and-effect relationships. A typical 800-word AI-generated news article contains 15–30 discrete claims. List them in a spreadsheet with columns: Claim, Source Required, Source Found, Verified/Unverified.
Step 2: Multi-Engine Verification
For each claim, query at least two independent sources. Use Omniscient AI's multi-engine fact-checker to run the claim against ChatGPT, Perplexity, and Gemini simultaneously — each engine will either corroborate with a citation or flag a discrepancy. Unanimous agreement across three engines with cited primary sources is the highest confidence level you can efficiently achieve in an editorial timeline.
Step 3: High-Risk Claim Deep Dives
Flag any claim where: engines disagree, no primary source can be found, the claim is a specific number (statistics are the most hallucinated element in AI text), or the claim involves a named individual's statement. Deep-dive these specifically by checking the original primary source document rather than relying on AI retrieval.