ANSWERS

Does content freshness matter to AI models?

Freshness matters for retrieval, not for trained knowledge. Publishing does not update model weights; retrieval can use newer sources without retraining. Neither the next crawl nor a new model version guarantees your page is included in an answer. A dateModified field that does not match a real content change is a poor signal. ye5's GEO Web audit checks whether your freshness signals match real content changes.

Why does freshness affect retrieval but not trained knowledge?

ye5's GEO Web audit treats freshness as one signal among many. Two separate clocks decide whether that signal reaches an answer.

A retrieval-grounded answer — what Claude, ChatGPT, and Gemini produce when they search live rather than answer from memory — can use newer sources without any retraining. A substantive content update becomes available to retrieval after a recrawl. A crawl does not guarantee the page is included in an answer.

A model's trained knowledge runs on a different clock (see: How long does GEO take to show results?). That knowledge came from a training run finished before the model shipped. Publishing does not update model weights. A new version may reflect your update, on a schedule outside your control, and inclusion is never guaranteed.

What counts as meaningfully fresh content?

A page is fresh when its facts changed, not when its date field changed. Freshness matters most for facts that change; an accurate evergreen page stays useful without edits. Three checks separate a real update from a cosmetic one:

—Content diff — the text, data, or examples differ from the prior version. A changed timestamp alone isn't a content diff.
—Materiality — the change affects a claim readers or models rely on: a price, a feature, a statistic, a policy. A typo fix doesn't count.
—Cadence match — the update pattern should track how often the underlying facts change. A weekly date bump on unchanged facts reads as noise.

How do you signal freshness correctly?

A freshness signal has two parts, and both need to match the real edit:

Signal	Where it lives	What it should reflect
Visible date	On-page, near the headline or byline	The date the content substantively changed
dateModified schema	Article or BlogPosting structured data	The same date as the visible one, not a separate, unsynced value
Change note	A one-line summary of the edit	Specific enough to show the update was real

Two dates can drift apart, and that is the common failure. A visible "updated" stamp with no matching schema change is one version. Schema that updates without the visible copy changing is the other (see: Which schema types matter most for AI?). Mismatched dates are an unreliable signal. Use honest dates that match the real edit.

FAQ

Does updating a page's date without changing its content help?

Updating a page's date without a matching content change does not help. A date should record a real content change. A bumped date on unchanged content gives readers and systems nothing new to work with.

Does an old page need to be deleted to stay AI-friendly?

An old page doesn't need to be deleted to stay useful. A page with an accurate publish date and updated facts still works as a citation source. Deleting it removes that source instead of fixing it.

How often should content be updated for freshness?

There's no fixed calendar for freshness updates. Update when the underlying facts change, and let the date follow the edit, not a routine. A page whose facts do not change can stay accurate as it is.