Large language models rarely browse your live site in real time; they lean on cached crawls, old sitemaps, and third-party mentions that may be months or years stale. When a sitemap still lists a discontinued service, a closed location, or superseded pricing, the model treats that as current fact and repeats it confidently to a buyer as a hallucination that looks authoritative. The fix is not hoping crawlers revisit sooner. It requires a maintained, machine-readable data layer: a fresh sitemap, JSON-LD that states current offerings and hours explicitly, and an llms.txt briefing that gives models a canonical, up-to-date summary to prefer over scattered stale sources. Unblocking AI crawlers so they can actually reach that fresh data matters just as much as writing it. Businesses that keep this layer current are described accurately; those that don't are silently misrepresented every time a prospect asks an AI assistant a real question about them.
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Training and retrieval snapshots are point-in-time. If your sitemap or pages haven't signaled a change since the last crawl, the model's internal representation and any retrieval-augmented lookup both default to the old version, so it repeats it as current fact.
No. Search Console resubmission affects Google's index, not the training or retrieval data other AI assistants use. Each model draws from its own mix of crawls, licensed data, and web retrieval, so correcting one does not correct the others.
Update the source page, refresh JSON-LD and llms.txt with the correct fact stated explicitly, ensure crawlers can reach it, then request a re-scan. Models weight consistent, structured, recently-touched data over ambiguous prose.
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