01Two Different Mechanisms Behind “Visibility”
“LLM visibility” is really two separate things happening at different times: whether your content shaped a model during training, and whether it gets retrieved live when someone asks a question a model can’t answer from memory alone.
Training-data presence is set at training time, changes on the vendor’s schedule (often months between major model updates), and can’t be targeted directly — there’s no submission process, and no way to verify what a private training run did or didn’t ingest. Live retrieval is the layer that behaves more like search: a browsing-enabled or search-augmented model runs something close to a real query, pulls a handful of pages, and writes its answer with that live context in front of it.
Most of what’s actionable in “LLM visibility” work is the second mechanism, because it depends on the same discoverability and clarity fundamentals as any other retrieval system — the ones described more generally at semantic SEO.
02How Visibility Differs by Tool
The major assistants don’t source answers the same way, so a technique that works for one doesn’t automatically transfer:
| Tool | How it typically sources an answer |
|---|---|
| ChatGPT (with browsing/search) | Runs a live web search for time-sensitive or specific queries; otherwise answers from trained knowledge with no live citation |
| Claude (with web search) | Retrieves and cites live pages when search is enabled; otherwise draws on trained knowledge, generally more conservative about unverified claims |
| Perplexity | Retrieval-first by design — nearly every answer is built from live-fetched sources with inline citations, making it the most directly comparable to classic SEO |
| Google AI Overviews | Draws from Google’s own index and ranking shortlist rather than an open web search — see AI Overviews |
Perplexity’s retrieval-first design means it rewards clean, crawlable, well-structured pages almost exactly the way a search engine does. ChatGPT and Claude lean more on trained knowledge for general questions, which makes sustained topical authority and third-party corroboration matter more than any single page’s structure.
03The Signals That Correlate With Getting Cited
The mechanics of turning “the model used my content” into “the model named me by name” are specific enough to warrant their own page — see AI citation optimization.
04The Honest Caveat: This Is Non-Deterministic
This is the same discipline described more broadly at GEO and on the AI search hub, applied specifically to chat-based assistants rather than Google’s own results page.