Every AI answer about your brand is built from two things: what the model learned during training, and what it retrieves from the live web at answer time. You can barely move the first, and you can move the second a lot. Training knowledge is baked into a model's weights and changes only when a lab retrains; retrieval happens fresh on each query and rewards pages that are reachable, relevant, and clean. So the practical answer to "how do I change what AI says about me?" is: focus almost entirely on the retrieval layer. This piece explains the split and where your effort actually pays off.
The framing draws on a widely-shared BrightonSEO April 2026 recap published by Peec AI, an AI-visibility analytics vendor, plus a 2026 network-traffic teardown of ChatGPT. Both point the same way: your chances of influencing what a model retrieves are far higher than your chances of influencing what it was trained on.
What is the difference between training data and retrieval?
A large language model holds two kinds of knowledge, and they behave completely differently for a brand trying to be described accurately.
| Training data (parametric memory) | Retrieval (grounding) | |
|---|---|---|
| Where it lives | Baked into the model's weights | Fetched from the live web at answer time |
| How fresh | Frozen at the knowledge cutoff | As current as the page it just read |
| How you influence it | Indirectly, over months, by being consistently described across the web | Directly, now, by being reachable, relevant, and citable |
| Shows up as | Confident answers with no sources | Answers that cite or link the pages used |
| Your leverage | Low and slow | High and fast |
The distinction matters because the two layers fail in different ways. If a model describes an old version of your brand with total confidence and no sources, that is stale parametric memory talking. If it searches, reads a competitor's page, and cites that instead of yours, that is a retrieval problem you can fix.
Why is retrieval the layer you can actually influence?
Retrieval is influenceable because it happens fresh on every query and reads whatever is reachable right now. Training is not, because you don't decide what a lab includes or when it retrains.
Most AI-search surfaces, from Google's AI Overviews to Perplexity, work by retrieval-augmented generation: they fetch relevant documents, then answer from what they fetched, then cite. That is why a page published this week can appear in an answer within days without waiting for a training cycle. Practitioner notes from BrightonSEO put it plainly: the battleground is prompts that trigger a web search, because those are the answers you can still shape.
Trained knowledge, by contrast, is set until the next model version. You can nudge it only indirectly, by being described consistently and correctly across enough of the web that the next training cycle learns the accurate version of you. That is real work with a long payoff, and it is worth doing, but it is not where this quarter's citations come from.
Where do AI models get their sources, and how do you become one?
One useful mental model from the conference notes: the engine is a judge, the retrieved sources are witnesses, and you are the lawyer who has to prepare and put forward the witnesses. You don't get to write the verdict; you get to influence which evidence is in the room.
To be a witness the engine calls:
- Be reachable. An AI crawler that can't fetch or render your page can't retrieve it. Server-rendered HTML beats content locked in JavaScript, PDFs, or images. This is the first filter in how ChatGPT picks sources.
- Be relevant at the passage level. Models don't read your whole page; they lift individual passages. Structure each section as a self-contained, answer-first chunk so the exact answer is easy to extract.
- Be corroborated. Entities named across multiple credible sources are the ones most likely to make it into an answer. One page asserting a fact is weaker than the same fact echoed across your site, reviews, and independent coverage.
- Be current. Grounding favours fresh pages, and citations decay after roughly a quarter, so competitive pages need a refresh cadence.
Does ranking in traditional search still feed retrieval?
Yes. Classic search rank and AI retrieval are correlated, and being visible in normal search raises your odds of being retrieved and cited, even though rank alone never guarantees it.
The Peec AI recap reports that a large share of URLs cited by Google's AI Overviews already rank in the traditional top 20, with the overlap notably lower for AI Mode, an indication that AI Mode reaches further down and wider than the classic results page. Treat those figures as directional single-source practitioner data, not audited benchmarks. The durable point is consistent with our own read that Google rank does not reliably get you cited: rank helps because a page that ranks is usually reachable, relevant, and indexed, exactly the traits retrieval rewards, but the AI engine re-selects at the passage level, so a well-structured page can be cited without a top rank, and a top-ranked page can be skipped.
You can't rewrite what a model already believes about your brand. You can change what it reads the next time someone asks. Spend your effort where the answer is still being decided: retrieval.
When does training data still matter?
Training still matters for two cases, so don't ignore it entirely. First, not every question triggers a search. A 2026 teardown of ChatGPT found it sorts questions into use-case buckets, and some are answered straight from trained memory with no web search at all, in which case only the model's parametric knowledge is in play. Second, the model's default associations, what it reaches for before any retrieval, are a training artefact, and being well-represented over time is how you become the brand it names by default.
The takeaway is not "ignore training." It is a matter of sequence and leverage: influence retrieval now, because you can, and let consistent, accurate, widely-corroborated presence across the web slowly improve what the next model learns. Being a clear, reachable, corroborated source about your own category is the one investment that pays into both layers at once.
Knowing which layer produced a given answer, and whether your retrieval work is actually moving citations, is hard to eyeball across engines. That is where Buffy Intel fits: it snapshots whether AI engines cite and recommend your brand over time, and shows whether an answer is grounded in pages it can point to or leaning on stale memory, so you can aim your effort at the layer you can actually move.