Spoke
Training data versus retrieval
One of these you can change this afternoon. The other changes when somebody else trains a model. Knowing which is which tells you where effort is worth spending and where it is wasted.
The mechanical distinction is documented by the platforms through their separate crawler tokens. What follows about where effort pays off is reasoning from that, not a published finding.
Ask a model about a well-known company and you may get an accurate answer with no links. Ask about something published last week and you get either a link or an apology. That difference is not a quirk of the interface. It is two entirely separate mechanisms.
Can I influence what an AI model already knows about me?
Not directly, and not quickly. What a model absorbed during training is fixed until that model is retrained, and no change to your site alters it. What you can influence is what the next training run would find — which means what independent sources say about you, not what you say about yourself.
Retrieval is the channel you can actually move. A page corrected today can be quoted today.
Why the distinction keeps mattering
A client sees a model state something outdated about their firm and asks how to fix it. The instinct is to update the website. If the answer came from training data, updating the website does nothing at all — the model is not looking at the website.
The useful questions are different ones. Did the answer carry a citation? If yes, it was retrieved, and the fix is on the cited page. If no, it came from the model, and the fix is slower: correct the third-party sources that describe you, and wait.
What actually feeds the training channel
Public, independent, durable text about you. Reference entries, press coverage, directory listings, forum discussion, transcripts, documentation other people wrote.
Note what is not on that list: your own marketing copy. It is in there somewhere, and it carries the weight of a party describing itself. The corroboration that moves the needle comes from sources with no stake in the description.
This is the long game, and it is genuinely slow. It is also the reason the off-site authority work is not optional for an organization that cares about being described correctly.
What feeds the retrieval channel
Everything the rest of this site is about. Can the page be fetched. Can it be parsed. Does it answer the question in an extractable form. Is it current enough to be worth citing over an alternative.
That channel responds in days. Start there.
The common mistake, stated plainly
People spend months on content aimed at "getting into the training data" and skip the crawlability work that would make them citable this quarter. The first is largely outside your control and operates on a horizon of years. The second is entirely within your control and operates on a horizon of a crawl cycle.
More in GEO & entities
How AI engines retrieve
Retrieval is what lets a model quote something you published this morning. Understanding it tells you which levers exist — and which widely repeated tactics are aimed at nothing at all.
What Google documents
Google published a guide to showing up in its AI features, and a big chunk of it is a list of popular tactics it says you don't need. That makes it the most useful source in this field, and the most uncomfortable one.
Platform matrix
One row per engine, each with its own last-verified date and a link to the platform's own documentation. Where a row could not be verified, it says so instead of being filled in from memory.
Entity consistency
The highest-value work in getting quoted by AI is also the dullest. Describe your company the same way everywhere, give it one stable identifier, and stop handing machines reasons to hedge about who you are.
Comparative content
Comparison questions are among the most valuable a buyer asks and among the most common an AI engine is given. The format decides whether you can be quoted — a table in HTML can be, the same table as an image cannot.