Keeping AI-written product content true

AI-written store content can fail in predictable ways: it can invent product detail, borrow health claims your evidence cannot support, cite sources that do not say what the sentence says, repeat an article you already published, or link to pages that no longer exist. Automated checks against the live catalog, source pages, the publication archive and live URLs can catch specific mismatches. They cannot prove that every statement is true when a source is incomplete, a claim is ambiguous or editorial judgment is required. This guide names ten checks that reduce those risks and the failures that should stop automatic publication.

For head-to-head buying advice, use the product-comparison claim ledger to keep documented facts, observations and unknowns separate.

The failure modes

Invented product detail

The most common and the most damaging. A draft says a serum contains niacinamide, or that a jacket is fully lined, or that a bag holds fifteen litres. It reads exactly like every true sentence around it. Nothing in the prose signals that the model filled a gap.

This is worse than an obvious error because customers act on it. A buyer who receives a product that does not match the description you published has a legitimate complaint, and the description is on your domain with your name on it.

Health and performance claims

A model trained on the whole internet has absorbed a great deal of confident language about what ingredients do. Dropped into your copy, that language becomes a claim you are making.

The FTC's position is unambiguous. Claims "about the health benefits or safety of foods, dietary supplements, drugs, and other health-related products require substantiation in the form of competent and reliable scientific evidence", which it defines as "tests, analyses, research, or studies that (1) have been conducted and evaluated in an objective manner by experts in the relevant disease, condition, or function to which the representation relates; and (2) are generally accepted in the profession to yield accurate and reliable results" (Federal Trade Commission, Health Products Compliance Guidance, read 9 September 2026).

Two details matter for automated copy in particular. First, implied claims count: "a marketer is equally responsible for the accuracy of claims suggested or reasonably implied in advertising", assessed on "the net impression conveyed by all elements of the ad, including the text, product name, and any charts, graphs, and other images". A model is very good at implying. Second, hedging does not save you: the guidance says qualifications must be "clear and conspicuous", and that vague modifiers such as "may", "helps", "preliminary" or "promising" are not enough to qualify a claim built on thin science.

If you sell in a regulated category, read that guidance before you automate anything. The category page for supplements covers the article types that work when strong claims are off the table.

Sources that do not say it

A model will attach a citation to a sentence the source does not support, or cite a page that does not exist. This is more dangerous than an unsourced claim, because the citation makes a reader stop checking.

Repeats of your own archive

Publish for six months and you will produce two articles answering the same question in different words. Neither is wrong, and together they are worse than either alone: they split whatever authority the topic earned and give a reader no reason to prefer one.

Google's spam policies name "scaled content abuse", which covers "using generative AI tools or other similar tools to generate many pages without adding value for users" (Google Search Central, Spam policies for Google web search, read 9 September 2026). The line is drawn at value and volume, not at the tool. A duplicate that adds nothing is on the wrong side of it.

Prose that reads like a machine

Template leakage ("As an AI language model", "Here is the article you requested"), exclamation marks, reading level that swings from a tabloid to a journal paragraph to paragraph. None of this is factually false. All of it costs you the reader.

Wrong or broken media and links

An image that shows a product with the wrong label or shape. A link to a product you delisted. Both are invisible to a proofreader reading for tone.

The ten checks

Citelift runs ten checks on every article before it can be scheduled, and each returns a result whether it passes or fails, so the merchant sees the whole checklist rather than a summary. You can run the same ten by hand or in your own pipeline. The order below is the order they run.

1. Claims

Every claim in the body, including the FAQ, is checked against what the brand is allowed to say and what it can support. A claim with no backing in your brand facts or a verified source fails.

Do this manually by listing every sentence that asserts an outcome, then striking any you cannot point to evidence for. Your product description drafts can be pre-screened with the product description scorer before they reach a page.

2. Product truth

Every product name, attribute and detail in the draft is checked against the live catalog. Not against a copy from last quarter. Against what the store actually sells now.

This is the check that catches the invented ingredient, and it is the one most hand-run processes skip, because it is tedious and the errors look correct.

3. Citations

Every statistic needs a source within a sentence or two of the claim, and the source has to be real and reachable. A number with a citation three paragraphs away is not verifiable by a reader in the moment, and a citation to a page that does not exist is worse than none.

4. Duplication

The draft is compared against what the store published over the previous ninety days. A near-repeat of a recent article is held rather than published, because the fix is editorial: merge, redirect or pick a different question.

5. Structure

Citelift's current generated-article guardrail requires four to eight H2 sections and normally two to five distinct eligible catalog products. When the configured catalog has only one eligible product, the gate allows one. The article type also supplies its own word range and required blocks. These are product-configured controls for keeping generated drafts within the chosen format, not universal SEO rules: one product link does not by itself make a useful guide a sales page, and section count alone does not establish depth or quality. The reasoning behind the working ranges is in product-linked blog articles that earn AI citations.

6. Readability

Reading grade inside the band the brand writes at, with tolerance for the fact that headings and list items measure as short sentences. Template leakage phrases fail outright. More than one exclamation mark in a whole article fails.

7. Image

The hero image has to actually depict the product it claims to. A generated image that alters a label, a shape or a colour fails, and the safe fallback is the real product photograph.

8. Links

Every link in the article has to resolve. Product links, source links, internal links.

9. Media

This check reads the media the article promises: a hero image that passed the image check, and a video that was fetched and confirmed to exist rather than a plausible-looking embed code. A dead embed is a broken page element a reader sees before they read a word.

This check is advisory and does not hold an article. A store whose catalog has no usable photographs would be held by it forever, with nothing anyone could edit to clear it, so a hero or a video that could not be verified is stated on the draft and the article publishes without it. Read that warning before you publish; it is the check most worth a human glance, because a missing picture is the failure a reader notices first.

10. Editorial

The first nine checks compare the draft against specific catalog, source, archive, format, link or media evidence. Their results can expose defined mismatches, but they do not prove every statement true or decide whether the article is worth publishing. The last check is a second reader, judging facts, prices, claims and usefulness — whether the draft answers the question it was written for, whether its confident sentence about a price is supported by the catalog, whether the proof it offers was invented. It reviews and annotates: what it thought is written onto the draft for you to read, and it does not hold the article. Its thresholds were derived on a different kind of evidence and are being recalibrated on real articles before they are allowed to cost anyone a publication.

Do this manually by rereading the draft once against the question you set, rather than against the outline. It is the pass most hand-run processes skip, because the article reads fine.

What to do when a check fails

Have one rule and follow it: cap automatic correction, then hold unresolved failures for a person.

Citelift can make up to two bounded corrective passes when a failed check has a precise repair: patch a passage, replace a source or research missing video. Every accepted change is rendered and all gates run again. Duplicate topics and catalog permission failures remain held for a person because a rewrite cannot decide them. Verified media is reused, and an unavailable hero receives at most one recovery attempt while the safe draft and product-photo fallback remain available.

Review hold is available on Core and Growth. Starter articles can publish only after all required quality gates pass, without the optional hold window. In any workflow, an unresolved required failure should stop publication regardless of plan.

The reason to cap correction attempts is that a loop hides the problem. An article that keeps failing a claims check is telling you something about the brief, catalog data or claim itself. Publishing after repeated rewrites would bury that signal.

Keep the failure reason attached to the article after it publishes. When a customer disputes a description months later, the record of what was checked and what was overridden is what you will want.

A worked example

Take a sample store selling a three-product skincare line. A draft comes back answering "what to use on combination skin in winter". It reads well. Then the checks run.

The claims check flags a sentence saying a cleanser "repairs the skin barrier". The brand facts support "supports the skin barrier" and nothing stronger, so the sentence fails and is rewritten. The product truth check flags a listed ingredient that is not on the current formulation, because the store reformulated in spring, and the draft used language from an older description. The citations check flags a percentage attributed to a dermatology journal with no reachable source, and the sentence is cut rather than rescued. The links check flags a product link pointing at a variant URL that no longer exists.

Four catches on one article that a human proofreader reading for tone would have approved. That is the point of running checks mechanically rather than reading carefully.

Disclosure and authorship

Say who is behind the content. Google's framework asks "Is it self-evident to your visitors who authored your content?" and, on automation, "Is the use of automation, including AI-generation, self-evident to visitors" (Google Search Central, Creating Helpful, Reliable, People-First Content, read 9 September 2026). The same page states plainly that using automation "to produce content for the primary purpose of manipulating search rankings" violates the spam policies.

Neither of those says do not use the tools. They say be the publisher. A short editorial note saying how articles are produced and who reviews them costs a sentence and settles the question for a reader who wonders.

The standard worth holding

An article on your domain is a statement your store is making, whatever typed the first draft. The checks above exist so that statement survives being checked by a customer, a regulator or an assistant deciding whether your page is worth quoting. Being quotable and being accurate are the same project: an AI citation is worth having only if the sentence being quoted is true.