AI VISIBILITY SUITE
ANSWERS
GEO moves on two clocks. Retrieval-based answers — what Claude, ChatGPT, and Gemini pull from live web pages — shift within weeks of a fix, since crawlers recrawl often. Trained-knowledge answers — what a model already knows — change when the next model version ships, months apart. The same fixes feed both, so the prescription is the same: start today. Improvement starts in weeks and compounds from there. ye5's audits show where you stand on each clock.
GEO — Generative Engine Optimization — is the work of shaping how Claude, ChatGPT, and Gemini describe and recommend a brand. AI answers draw on two different sources, and each source moves on its own clock. Retrieval-based answers pull from your live pages at the moment someone asks a question. Trained-knowledge answers pull from what the model absorbed during training, with no live lookup involved. The two never move together.
| Answer type | What it draws on | How fast it moves |
|---|---|---|
| Retrieval-based | Live pages fetched at answer time | Weeks, as crawlers recrawl and pages update |
| Trained-knowledge | What the model absorbed during training | Months, tied to the model's release cycle |
Crawlers find your changes fast. ClaudeBot rechecks a site roughly every three hours for discovery . Googlebot and GPTBot run their own frequent passes. Discovery is not the same as visibility. A crawler can fetch a fixed page within hours. The answer engine can still take weeks to surface that fix. Indexing, ranking, and the next round of user queries sit between a crawl and a quoted answer. Template-level fixes compound the effect. Site-structure work tends to show retrieval-based movement faster than one-off page edits.
Trained-knowledge answers describe what a model already knows about your brand from its training data. That knowledge does not update between releases. No amount of on-site work moves it faster — but the work you publish now is what the next version learns from. The model isn't re-reading your site to answer. It is recalling what it absorbed during training. New model versions carry updated training cutoffs and shift this baseline. Release cycles run months apart, not weeks. If your gap sits in trained knowledge, results timing follows the model's calendar, not yours.
No. The same fixes feed both. Retrieval reads your live pages now; the next model version trains on the same pages later. Publish the correct, machine-readable facts once and both clocks start moving — one in weeks, one at the next release. Waiting for the slow clock means missing the fast one. ye5's GEO Web audit finds the patterns suppressing retrieval-based visibility (see: My SEO is strong — why is my AI visibility bad?). ye5's GEO Brand audit shows where the trained-knowledge picture is wrong or missing. Knowing which clock you're waiting on keeps you from mistaking normal lag for a failed fix.
This page answers how long results take once you start fixing gaps. It does not answer how often to re-check your standing (see: How often should I audit AI visibility?). Cadence is about measurement frequency. Results timing is about how fast the world responds to work already done.
Yes. Retrieval-based answers pick a fix up within weeks. The next model release trains on the same corrected pages. Nothing on your end speeds the release cycle, and nothing you publish today is wasted on it either.
No — audits measure where you stand, and measuring doesn't speed up crawlers or model releases. A tighter audit cadence catches changes sooner; it doesn't cause them faster.