Human review of AI text often stops at spelling and style. The real risk lies in its claims: numbers, dates, product features, legal wording and causal relationships. A claims ledger makes it visible exactly what needs verification.
Divide the text into verifiable units
Record every statement that can be true or false independently of the others. A paragraph can contain four distinct claims and one opinion. Checking it as a single block makes it easy for a mistake to hide among correct information.
Classify the risk
Prioritise health, safety, finance, legislation, prices, availability and allegations concerning people or companies. A metaphor or explicitly identified opinion may carry less risk. The classification determines how much evidence and which level of approval are required.
Connect each claim with evidence
The ledger records the source, access date, relevant excerpt, review owner and status. For original frameworks, identify them as editorial methodologies rather than external facts. For current news, use the primary announcement and make its origin clear.
State the level of certainty
Distinguish confirmed facts, reasonable estimates, working assumptions and unknowns. If two credible sources disagree, that disagreement is part of the story. Content becomes stronger when it acknowledges the limits of knowledge instead of filling gaps with certainty.
Recheck information that changes quickly
Prices, terms of service, executives, software capabilities and regulations have a shelf life. Add a review field and trigger a warning before an article is left exposed with outdated information. An update should meaningfully change the text and be recorded.
Assign personal accountability
The model cannot sign off on accuracy. Every critical claim needs a person who has examined it, understood the source and approved the wording. Review then becomes a repeatable editorial operation rather than a general promise.
The claims ledger is an original editorial review framework developed by DigitalNow.