What is Fact-correction loop?

The fact-correction loop is the repeatable four-stage workflow for remediating wrong facts an AI engine states about a brand: detect, diagnose, correct, verify. Detect: run a brand-fact query set (‘how much does X cost’, ‘does X have feature Y’, ‘who founded X’, ‘is X the same as Z’) across the engines buyers use, on a cadence, logging every stated fact against ground truth.

Diagnose: determine the error class (stale, fabricated, conflated, mis-attributed) and whether it is training-baked or retrieval-time, using the test of asking with and without letting the engine browse. Correct: establish a loud, machine-readable ground-truth surface on the entity home; mark the facts up with Product, Organization, and FAQ schema; reconcile the stale third-party sources stating the wrong fact; fix the entity graph for conflation errors; and use any direct-correction channel the engine offers. Verify: re-run the query set after propagation and log the time-to-correct per engine. The loop is standing rather than one-off — corrected facts do not stay corrected, because pricing changes, features sunset, new stale sources appear, and engine refreshes can resurface a fixed fact — so it folds into a monthly monitor alongside share-of-voice tracking.