Fact-check the fields AI engines quote

A fact-check of AI-generated content is a human review of every claim a model wrote before it is published, and Google now applies that expectation to the small fields (titles, descriptions, structured data, alt text) as well as to the body copy.

The change landed on October 1, 2026. Google rewrote the accuracy section of its guidance on using generative AI content, adding three sentences. The first explains that generative models "don't retrieve facts, but predict a likely sequence of words based on their training data." The second says outputs may contain hallucinations. The third is the instruction: "It is critical to manually factcheck and review all AI-generated content for accuracy and trustworthiness before publishing." Search Engine Journal's write-up notes that the review now explicitly covers title elements, meta description elements, structured data and image alt text.

None of this is a new ranking rule. PPC Land points out that no enforcement mechanism came with the edit, and Googlers had already said similar things in talks: Gary Illyes said in April 2024 that generative AI output "always has to be fact-checked," per the SEJ article. What changed is that the advice is now written into the documentation, next to a list of fields that most teams never review by hand.

#Why the small fields matter more now

Most editorial review happens on the article. The fields Google named are the ones that get generated in bulk: a script writes meta descriptions for every category page, a product feed tool fills in alt text, a plugin produces FAQ markup from the page. These fields are short, numerous and rarely read by a person after they ship.

They are also the fields that get reused out of context. A title and a meta description are the raw material for a search snippet. Structured data feeds rich results and product listings. Alt text describes an image to anyone, or anything, that cannot see it. Each one is a compact claim about the page, detached from the surrounding text that might have corrected it.

A wrong number in paragraph six of an article has some chance of being caught by a reader or contradicted by the next paragraph. A wrong number in Product.offers.price, or a meta description that promises a feature the product does not have, travels alone.

#Grounded engines repeat what you publish

The case for checking these fields gets stronger once you look at how answer engines use source text. Systems like AI Overviews, ChatGPT search and Perplexity retrieve pages and then summarize them. On that task, current models are fairly faithful to the input. Vectara's hallucination leaderboard, which asks models to summarize more than 7,700 documents using only the facts in each document, shows most competitive models in roughly the 5% to 15% hallucination range, with the best at 1.8% (leaderboard updated September 22, 2026).

Read that from the publisher's side. If a summarizer stays close to the source most of the time, then the accuracy of the summary depends heavily on the accuracy of the source. A hallucinated spec in your markup is not filtered out by a grounded engine. It is the grounding.

The engines also add errors of their own. The EBU and BBC study of more than 3,000 responses from ChatGPT, Copilot, Gemini and Perplexity found that 45% of answers had at least one significant issue, 31% had serious sourcing problems and 20% had major accuracy issues, including hallucinated details and outdated information. That was a news-content test, so the rates will not transfer directly to product or service queries. The direction still holds: there is already an error layer between your page and the reader. Publishing unchecked machine text adds a second one underneath it, and the two are hard to tell apart when you are reading an answer that misdescribes you.

#Where the errors tend to hide

AI-written fields tend to fail in a few predictable ways.

• Invented specifics. A model asked for a compelling meta description will reach for a number: a year founded, a customer count, a delivery time. If the prompt did not supply the number, the model guessed it.
• Template drift. A generator fills the same field across thousands of URLs. When one input is missing, the output often borrows from a sibling page, so a product gets another product's dimensions.
• Markup that disagrees with the page. FAQ or product schema generated separately from the visible content can state a price, rating or availability the page does not show. Google's structured data guidelines already require markup to describe visible content. A generated block is the easiest place to break that rule without noticing.
• Alt text that describes the wrong thing. Image captioning models describe what the image appears to show, which is not always what the image is. A photo of one model of a product, captioned with the name of another, is a factual error in the one field built to describe the image.

#A review process that fits the volume

Reading every generated field by hand does not scale for large sites, and Google's wording does not say how review should be done. A practical version splits the work by risk.

1. Inventory the generated fields. List every field written by a model or a template: titles, descriptions, each schema type, alt text, feed attributes. Record which tool writes it and from what inputs.
2. Ban unsupplied facts in prompts. Instruct generators to use only values passed in from your own data. Then check outputs for numbers and proper nouns that do not appear in the input. That check is mechanical and catches the most common invented specifics.
3. Diff markup against the rendered page. Compare structured data values (price, availability, rating, dates) with what the visible page shows. Any mismatch is either a generation error or a page error, and both need fixing.
4. Sample by template, read by hand. For each template, have a person read a sample of generated fields against the page, with the sample weighted toward high-traffic URLs and toward pages where an error would mislead a buyer or patient.
5. Check what the engines say back. Ask the answer engines about your products and services and compare their statements with your source data. When an answer is wrong, trace it to a cited URL and check whether the error started on your site. Of all the steps, this one tells you whether the earlier steps worked.

#The practical reading

Google's edit does not change how anything ranks today. It does put on record that the fields teams generate in bulk are content, held to the same accuracy standard as the article. Answer engines treat them that way already: they read your markup and descriptions as statements of fact and repeat them with fair fidelity. Check those fields first, because they are the ones most likely to have skipped review and the ones most likely to be quoted.