# Template review with a swapped noun Part of [The AI Tells Index](https://feedsquad.com/ai-tells). A tell signals low effort. It does not identify an author. Skilled writers produce every shape listed here, some of them daily, and automated detectors misread those writers for it at rates measured above 60 percent on non-native English prose. Nothing in this index proves that a machine wrote anything. Read an entry as one piece of evidence to weigh against the false-positive notes printed beside it. ## Facts - Id: template-review-swapped-noun - Category: Platform-specific (platform) - Subcategory: review-shape - Also known as: same review, different product, cross-product text reuse - Status: Active. Currently signals low-effort writing. - Severity: high - Evidence grade: corroborated (named independently by multiple credible secondary sources) - Languages: en - Added: 2026-08-15 - Updated: 2026-08-15 - Page: https://feedsquad.com/ai-tells/template-review-swapped-noun ## Description The same review text appearing across unrelated products with the product noun exchanged. Pangram, a detector vendor, reported on 4 May 2026 that about 3 percent of front-page reviews on 500 best-sellers were machine-written, 909 of 30,000 across ten categories, and that 93 percent of those carried a verified-purchase badge. The badge number is the one worth keeping: purchase verification does not separate a review a person wrote from one a model wrote. The vendor also describes those reviews clustering at the ends of the star scale, higher at five stars and higher at one star than human reviews; the figures behind that stay with the vendor and are not reprinted here. ## Why it reads as machine-written A template with a slot is the cheapest way to produce a review at volume, and the slot is almost always the product name. Everything around it stays fixed because nothing around it was ever about the product. Amazon's own detection works on behaviour and account graphs rather than prose, which is a standing reminder that text reuse is the amateur signal and the professional one is the account behind it. ## Detection Type: statistical (a measured metric against a threshold with a stated basis) Metric: cross-product-review-text-reuse-ratio Threshold: 0.7 (fires when above) Threshold basis: > No published cutoff exists for review-to-review text reuse. Seventy percent shared word 5-grams between two reviews of unrelated products is a FeedSquad review trigger, set high enough that a prolific reviewer's personal formula, which typically reuses an opening and a sign-off, stays below it while a swapped-noun template lands above. Compare after lowercasing and removing the product name. The Pangram study measured how common machine-written Amazon reviews are, not how much text they share, so this number is ours and not theirs. ## Examples Constructed specimens. Written for this index. Never quoted from anyone's posts. ### Specimen 1 Before, exhibiting the tell: > This blender does exactly what I needed and the build quality is solid. It arrived quickly and was easy to set up. I would recommend this blender to anyone looking for a reliable option at this price point. After, repaired: > The 900W motor stalls on frozen strawberries unless you pour in liquid first, which the manual does not mention anywhere. Two months in, the gasket has taken on the smell of whatever went through it last and does not wash out. Fine for smoothies, wrong for nut butter. Note: The repair is specific to one object, so it cannot be reused by swapping a noun. Figures in this repair are invented for the specimen. ## False positives Who legitimately writes this way. Prolific honest reviewers develop a personal formula and reuse it, because writing 400 reviews without a shape is exhausting. Reviewers who buy several units of the same product legitimately write near-identical text about them. The discriminator is reuse across unrelated products, not the presence of a formula, and even then a shared account and a shared household explain a lot. This entry never supports a claim about who wrote a given review. ## Model attribution No family attribution. The vendor that produced the prevalence figure sells detection and does not publish which model families it attributes text to for this dataset. ## Platform notes - amazon: Amazon describes its own fake-review detection as behavioural and graph-based, looking at review history, risky behavioural patterns and groups of connected bad actors. Its published material does not name AI-generated reviews or any stylistic criterion at all, which is the strongest available argument that text-level review tells are a weak instrument. ## Sources 1. Pangram, AI-generated Amazon reviews (4 May 2026) https://www.pangram.com/blog/ai-amazon-reviews (tier: vendor; accessed 2026-08-14) 2. FTC 16 CFR Part 465, rule on consumer reviews and testimonials https://www.ftc.gov/legal-library/browse/federal-register-notices/16-cfr-part-465-trade-regulation-rule-use-consumer-reviews-testimonials-final-rule (tier: primary-doc; accessed 2026-08-14) ## Status history Ids are permanent. A retired tell keeps its id and its page. - 2026-08-15, Active: One vendor measurement and one federal rule support the entry, which is corroboration across independent kinds of source rather than a single sighting. Severity is high because fake reviews carry money and regulatory consequences, not because the prose is dull. ## License CC BY 4.0. https://creativecommons.org/licenses/by/4.0/ Attribution: The AI Tells Index, feedsquad.com/ai-tells Reuse the data, including commercially. Keep the attribution line. --- Dataset version 1.0.0. Schema version 1. Part of [FeedSquad](https://feedsquad.com). Built by [Herman Foundry](https://hermanfoundry.com) from Levi, Finnish Lapland.