AI training vs AI retrieval: two different ways to be used

The distinction in one sentence

Training decides whether a model has any latent idea who you are; retrieval decides what it says about you today. They run on different crawlers, they respect different controls, and they fail in opposite directions.

Two mechanisms, described by the people who built them

The clearest description of the mechanism difference is not from a marketing blog but from the 2020 paper that introduced retrieval-augmented generation. It notes that large pre-trained language models store factual knowledge in their parameters, and that their ability to access and precisely manipulate that knowledge is still limited. It goes on to name the two problems that follow: providing provenance for their decisions, and updating their world knowledge, remain open research problems.

That is the training side of this comparison, stated by researchers with no reason to soften it. Whatever a model absorbed is in the weights, hard to inspect, hard to attribute and hard to correct.

Retrieval is the proposed fix. The same paper defines RAG models as those which combine pre-trained parametric and non-parametric memory for language generation: the model keeps its parameters, and gains an explicit external memory a retriever consults while generating. The reported result is that such models generate more specific, diverse and factual language than a parametric-only baseline, and set the state of the art on three open-domain question answering tasks[^rag-factuality].

For anyone trying to be described accurately by an assistant, that architecture is the whole opportunity. Retrieval is the channel through which a current fact about your product can reach a current answer.

The crawlers are different, and that is the practical part

Because the two mechanisms are separate, the crawlers are separate, and the vendors document them separately. This is where most sites make an expensive mistake.

OpenAI's crawler documentation names GPTBot as the crawler used to make its foundation models more useful and safe, crawling content that may be used in training, and says that disallowing GPTBot indicates a site's content should not be used in training generative AI foundation models. It names OAI-SearchBot as the crawler used to surface websites in ChatGPT's search features, and states that sites opted out of OAI-SearchBot will not be shown in ChatGPT search answers. And it names ChatGPT-User as the fetcher used for certain user actions, visiting a page when a user asks a question, adding that because these actions are initiated by a user, robots.txt rules may not apply.

Google's crawler documentation does the same on its side of the fence. Google-Extended is described as the way site owners manage whether content Google crawls may be used for training future generations of Gemini models, and Google states plainly that Google-Extended does not impact a site's inclusion in Google Search nor is it used as a ranking signal in Google Search.

Read those two documents together and the operational rule writes itself. A blanket rule aimed at "AI crawlers" does not express a policy. It expresses the absence of one, and it usually costs you the retrieval channel you wanted to keep.

The asymmetry that should drive your decision

The stated cost of each opt-out is wildly different, and both vendors have put their version in writing.

Opting out of Gemini training, on Google's own account, costs you nothing in Search: no inclusion effect, no ranking effect. That is close to a free choice, whatever you think about model training.

Opting out of ChatGPT's search crawler, on OpenAI's own account, removes you from ChatGPT search answers. That is not a free choice at all. It is a decision to be absent from a surface where buyers ask comparison questions.

So the two decisions have different prices and should be taken at different levels of the business. The training question is a rights and positioning question. The retrieval question is a distribution question.

Why a model gets your product wrong, and which mechanism can fix it

A model that describes a plan you retired eighteen months ago is showing you parametric knowledge. Publishing a correction does not reach into the weights, and the paper says as much: updating world knowledge remains an open research problem. The fix is not to publish harder. The fix is to make sure retrieval can override the stale memory, which means the current fact must live on a page the retrieval crawler can reach, stated plainly enough to be lifted.

That is a very specific writing instruction. The correct current price, in words, in a sentence that names the plan and the currency, on a page that is crawlable and snippet-eligible, beats any amount of narrative about your pricing philosophy. The same goes for feature availability, supported platforms and anything else a model has previously got wrong about you.

Where the two mechanisms meet: entity knowledge

The one thing training does that retrieval cannot is give a model a prior. If a model has never encountered your brand name in any form, retrieval has to do all the work from a cold start, and the system has no reason to treat you as a plausible answer to "what tools do people use for this".

That is the honest argument for not blocking training wholesale if your goal is visibility. Category familiarity, entity resolution, knowing that your name is a company rather than a typo — those come from breadth of exposure over time, mostly through third parties rather than your own site. It is also why off-site presence matters more than most on-site work for this particular effect.

How to tell which mechanism produced an answer you are looking at

You can usually diagnose this from the answer itself, and it is worth doing before you decide what to change.

If the answer carries links to current pages, retrieval was involved. A source list is the visible artefact of an external memory being consulted. Whether the cited page actually supports the sentence next to it is a separate question, and frequently the answer is no, but the presence of links tells you the system went and looked.

If the answer is confident, unlinked and slightly out of date, you are looking at parametric knowledge. The tell is specificity about things that were true a while ago: a former plan name, an old integration list, a founding claim that has since changed. No amount of republishing fixes that directly.

If the answer is confident, unlinked and completely wrong, check whether a third party is the source. Retrieval can be reaching a review site, a roundup or a forum thread rather than your own pages, in which case the correction has to happen where the wrong claim lives, not on your site.

If the answer says it cannot find information about you, check your robots rules first. That symptom is far more often a blocked crawler than a content problem, and it is the cheapest thing to rule out.

Running the same prompt several times sharpens all of this, because a system that retrieves will tend to vary its sources between runs while parametric recall stays eerily consistent. Consistency across many runs, with no links, is the signature of a fact the model simply believes.

What each mechanism rewards in your writing

Retrieval rewards extractability. A retriever is matching a question against passages, and a generator is lifting the passage that answers it. Short, self-contained, declarative sentences under question-shaped headings are the unit that survives that process. A sentence that depends on the previous paragraph to make sense cannot be lifted, and a paragraph that hedges every claim gives the generator nothing to assert.

Training rewards repetition and consistency across sources. It is a statistical process over an enormous corpus, so what it absorbs about you is whatever the web says about you most consistently. If your own site calls you one thing, your documentation calls you another and third-party listings call you a third, none of those descriptions is reinforced. Saying the same true sentence about your category in the same words, everywhere, is the closest thing to a training-side lever a small company has, and it is slow.

Notice that these two do not conflict. Clear, consistent, specific sentences serve both. That is convenient, and it is the reason this comparison rarely produces a real trade-off in the writing, only in the crawler policy.

A policy you can defend

Decide the two questions separately and write the answers down.

For training, choose a position and apply it consistently across GPTBot, Google-Extended and any other operator's training token, knowing that Google says its own opt-out costs nothing in Search.

For retrieval, allow it unless you have a specific reason not to. Keep pages crawlable and indexable, keep them snippet-eligible because Google names that as the prerequisite for its AI features, and remember that a user-initiated fetcher may reach you regardless of your robots rules.

Then check the thing almost nobody checks: read your own robots.txt and confirm which of these agents you are currently blocking, and whether that matches the policy you just wrote. In most cases it will not, because the rules were added during a period when the distinction between training data and retrieval was much less clearly documented than it is now.