Indexing Latency: How Quickly Major LLMs Refresh Their Local Knowledge Bases

Unlike a search engine that can re-crawl and reflect a page change within days, large language models rely on a mix of pretraining snapshots and live retrieval, and the two behave differently. Core training knowledge can lag reality by months, which is why an outdated address sometimes surfaces confidently in an answer. Live retrieval layers, used by web-browsing ChatGPT, Gemini, and Perplexity-style lookups, close some of that gap by pulling current pages at answer time, but only if those pages are structured clearly enough to be parsed and are not blocked from the crawlers fetching them. The practical implication is that visibility work has two timelines: a near-term one, where clean structured data and an accessible llms.txt briefing let live retrieval surface accurate answers within weeks, and a longer one, where trained knowledge eventually catches up. AI HALO's 30-day re-scan tracks the retrieval-layer lift you can influence, not the retraining cycle no one controls.

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Questions

Answered.

Why does ChatGPT sometimes describe our business using outdated information even after our site changed?+

If the answer came from the model's trained knowledge rather than a live browsing lookup, it reflects a snapshot from whenever that data was collected, not your current site. Only retrieval-enabled queries pull fresh information at answer time.

Which AI assistants actually fetch live pages versus relying on stored training data?+

Behavior varies by product and by query, and providers change this frequently. In practice, assistants with browsing or search features will fetch current pages when the query calls for freshness, while a plain conversational answer may fall back to trained knowledge alone.

Is there any way to force faster indexing of a business update?+

There is no submission mechanism that guarantees speed, and no legitimate way to push content directly into a model's training data on demand. The reliable lever is making pages maximally easy for retrieval systems to fetch, parse, and trust when they do check.

Every AI model updates its knowledge of your business on its own delayed schedule.

Indexing Latency: How Quickly Major LLMs Refresh Their Local Knowledge Bases

Answered.