The short answer: through two channels. First, training data: the model memorized patterns from years of web pages, articles, reviews, and forum threads written about you before its knowledge cutoff. Second, live retrieval: at answer time, most engines search the web and read current pages before writing. Everything an AI says about your brand — accurate, outdated, or invented — traces back to one of these two channels, and they respond to very different work on your part.
Channel one: what the model memorized
During training, an LLM ingests a vast snapshot of the web and learns statistical associations from it — including which brand names co-occur with which categories, problems, and praise. If the training data contained hundreds of independent pages describing you as "accounting software for freelancers," the model absorbs that association and can reproduce it with no internet access at all.
Three properties of this channel matter for GEO:
- It's frozen. Everything memorized reflects the web as of the model's knowledge cutoff. Rebrands, launches, and price changes since then don't exist in the weights — a common source of confidently outdated answers.
- It's weighted toward corroboration. One brand describing itself is a whisper; fifty independent sites agreeing is a signal. Models "know" best the brands the web talks about consistently — why digital PR is a training-data strategy, not just a publicity one.
- It's ambiguity-sensitive. If your name collides with other entities or your facts differ across sources, the associations blur. Entity SEO — one name, one description, the same facts everywhere — is how you sharpen them.
Channel two: what the engine reads right now
Memory alone would make AI permanently stale, so modern engines bolt on live search: for current, commercial, or specific questions, they issue web queries, read the top results, and ground the answer in what they find — retrieval-augmented generation. Perplexity does this for nearly every answer; ChatGPT, Gemini, and Claude do it whenever freshness helps.
This channel is only as old as its sources. A page published this week can be fetched by an AI crawler, read into an answer, and credited with a citation days later. It's also where third-party content dominates: for "best X" questions, engines lean on the comparison posts and review roundups that rank — so what those specific pages say about you often matters more than what your homepage says.
Which channel you can influence fastest
Work backward from speed. The retrieval channel moves in days to weeks: publish direct, citable pages, keep facts current, add structured data, and earn placement in the sources engines already cite. The memory channel moves in quarters: sustained coverage and entity consistency compound into whatever the next model version memorizes. The same effort often serves both — a strong third-party review is retrievable today and training data tomorrow.
Diagnostically, the channels separate cleanly: answers with citations came through retrieval (fix the cited sources); uncited from-memory answers came from training data (build the footprint). The full mechanics of how engines turn these inputs into recommendations are in how AI engines recommend brands.
To see which channel is currently doing the talking for your brand — and what each engine actually says — run a free audit; it takes minutes and needs no signup.