In one sentence: Training data is the very large body of text and other material an AI model learns from before it is released, absorbed once and then fixed in place, which is why a model's built-in knowledge of any business can be months or years out of date.
Training is an event, not a subscription. A company assembles a huge pile of material, web pages, books, code, transcripts, and the model works across all of it until it gets good at predicting what comes next. Then training stops and the model ships. Nobody goes back and edits the pile afterward. The next version is a new pile and a new run.
Nothing in there is filed away as a record the model can look up later. It is closer to what a person retains after reading a library: the general shape sticks, the specifics blur, and the confidence stays high either way.
Two things follow from that. It cannot tell you where a fact about your business came from, because there is no source attached to anything it retained. And it cannot tell you the fact has gone stale, because it has nothing newer to compare against.
Every model has a point past which it absorbed nothing further. Ask an assistant for a specific hardware store's Sunday hours and, if the answer is coming only from training, it is repeating whatever the web happened to say back when that pile was assembled. That could be two summers ago. Ask again a year later and, if nothing else has changed, you get the same stale answer in the same steady voice.
Owners ask us how to get their business into the training data. There is no submission form for a business like yours, no queue, and no way to check whether you made it. The pile is assembled privately, largely out of what was already public, and it is not published for you to audit. Chasing it is not a strategy.
The one thing worth doing is the thing you would do anyway. Publish plain, current pages and leave them readable. Whatever ends up in a future model gets there because it sat on the open web at the right moment, not because anybody put in a request.
The better read on it is this. Training gives a model its rough background, but the answer your customer actually reads is usually assembled from a lookup done at the moment of the question. That lookup you can influence, and it reflects a change you make to your site the same week you make it. The gap between hoping to be memorized and being findable on demand is the whole argument in our plain-English guide to AIO.
Most replies you see are a blend: background from training, plus whatever got retrieved right then. The retrieval half has its own name, grounding. When a model leans too hard on the training half and states something confidently wrong, that has a name too, AI hallucination.
Knowing which half you are looking at is the useful part. If an assistant describes your business with no sources attached, you are reading its memory, and arguing with it changes nothing. If it lists the pages it opened, you are reading the live web, and the live web is a thing you can go and correct today.
Not after the fact. Training bakes the material into the model itself, and there is no delete key for one business. What you can do is block crawlers from taking fresh copies going forward and correct the live sources an assistant reads when somebody asks about you.
Not by itself. Volume is not the lever, agreement is. A handful of pages that say the same thing about what you do and where you do it are worth more than a pile of pages that quietly contradict each other.
What is AIO? · Large language model · Common Crawl · Grounding · AI hallucination · All glossary terms · Plain-English answers · AI search optimization services
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