I’m posting on behalf of AI Undetectable, a rewriting tool. This is a self-contained review exercise with fictional data. The rewrites below are constructed examples, not outputs collected from a model or a product test. Suppose the task is to shorten a production update while preserving each printer’s booklet count and pages per booklet. Source: “Printer A produced 18 booklets with 6 pages each. Printer B produced 6 booklets with 18 pages each.” Rewrite 1: “Printer A produced 18 six-page booklets; Printer B produced 6 eighteen-page booklets.” Rewrite 2: “Printer A produced 6 booklets with 18 pages each. Printer B produced 18 booklets with 6 pages each.” Rewrite 3: “Each printer produced 108 pages.” The second rewrite contains exactly the same numbers as the source. Even checking the total pages per printer would miss the error: 18 × 6 and 6 × 18 both equal 108. What changed is which quantity belongs to which fact. The third rewrite has correct totals, but it drops the packaging information the task required. It could be fine for a different task that asks only for total pages. A small reference checklist makes the distinction explicit: Printer A → booklet count → 18 Printer A → pages per booklet → 6 Printer B → booklet count → 6 Printer B → pages per booklet → 18 Against those four requirements: Rewrite 1 preserves all four, even though two numbers are now written as words Rewrite 2 changes all four assignments Rewrite 3 omits all four, while retaining a valid derived total That suggests a more useful review process than checking whether every numeral survived: Write down the facts the task requires before looking at the rewrite. Include the entity, what is being measured, the value, and the unit or grouping For each required fact, mark the rewrite as preserved, changed, omitted, or ambiguous. Quote the phrase that supports the judgment; use “no supporting phrase” for an omission Review any new claim separately. A correct calculation can still leave out required details, and a fluent sentence can attach the right value to the wrong entity Judge style after checking the required facts. Keep the factual checklist separate so a smoother sentence doesn’t compensate for an incorrect detail If you use another model to assist this audit, give it the source, rewrite, and explicit checklist. Ask for one judgment and supporting quote per fact, rather than one overall “meaning preserved” score. Check those quotes yourself. This example demonstrates a failure case for simple checks; it doesn’t establish how reliably any model will catch it. Project context and public editor: https://aiundetectable.com/ai-humanizer submitted by /u/TrekkingAround10
Originally posted by u/TrekkingAround10 on r/ArtificialInteligence