All numbers are in place, but the meaning has changed: how to verify AI edits

The article addresses a critical issue when using generative AI for text editing: maintaining formal accuracy while losing logical meaning. The author provides an example where an algorithm successfully retains all numerical values and names but swaps them, leading to factual distortion. Standard automated verification tools, focused on character-level differences, are unable to detect such semantic errors. The author emphasizes that automating editing requires implementing new verification methods that go beyond simple text comparison. To ensure data reliability after AI processing, it is necessary to use logical tests and condition checks to confirm that the meaning of statements remains unchanged. The article urges developers and editors to be vigilant and not rely solely on automated tools when dealing with critical information.