The 3 LLM Behaviors Your AEO Strategy Has to Optimize For

LLMs recall, retrieve, and infer. Each behavior needs a different play, and most AEO strategies are only built for one of them.

When it comes to AEO strategy, we don’t yet have prevailing wisdom from which to pull best practices. Those are still being formed. And it makes the work of proving impact for visibility initiatives both unpredictable and a bit nerve-wracking.

I would venture an educated guess that a lot of the activities marketers default to for AEO campaigns can be mapped to SEO strategies. That’s not a bad thing. But search engine optimization is incomplete when applied to large language models.

Here’s what I mean.

Think about why people use LLMs instead of just typing five to ten words into a search box. It’s because they can do a full contextual brain dump (tell the machine everything about what they’re trying to figure out, with or without a clear question) and let it sense-make. The human equivalent of that is empathy. You tell someone your whole situation so they can actually understand it. People do the same thing with LLMs.

But when it comes to getting found by those same systems, we revert. We find the keywords, structure the content, build the links, copy competitors. When we track outcomes with GA4, we call it SEO. If we’re tracking outcomes for those same actions using something branded as an AI visibility tool, we call it AEO or GEO. But the fundamental strategy remains unchanged.

What we’re not fully accounting for is that LLMs have multiple distinct behaviors. And those behaviors don’t all respond to the same strategy, because these are generative systems. The generative part doesn’t go away just because we’re using them for visibility instead of content creation. Some unpredictability is structural. What you can do is understand which behaviors you’re actually optimizing for and build a strategy that accounts for all of them.

In the absence of historical data and transparency, engineer visibility around behaviors: both your ideal customers’ and the LLMs they use to find you.

There are three LLM behaviors (ways AI systems access knowledge) that matter for your AEO strategy:

• Recall: what the machine already knows, encoded before a conversation starts
• Retrieval: what the machine goes out to find when it needs a current answer
• Inference: what the machine constructs when it decides it already knows enough

Three different behaviors. Three different optimization requirements. And a strategy built for one of them is likely to underperform, or miss entirely, against the other two.

Developers know these as pre-training data, RAG, and inference. That’s the architecture layer. What I’m mapping here is the founder layer: what these behaviors mean for brands and businesses that aren’t building AI, but are being evaluated, recommended, or ignored by it.

RAG and live retrieval are consolidated here because they require the same strategic response from a brand. Inference gets its own category because the visibility failure it creates is distinct, underdiagnosed, and as far as I can tell, undervalued in the marketing and brand strategy space.

Your visibility strategy has to be built around how these systems actually behave. Not around a playbook written for a different machine.

Alright, let’s get into it.

How AI systems actually find you

Large language models use three distinct behaviors to find and surface information. Each one works differently, and each one requires a different play. Most AEO strategies are only built for one of them.

LLM behavior 1: Recall, what the machine already knows

Recall is what happens when a machine answers from what it already knows. No search, no retrieval, no real-time pull. This is parametric knowledge from training data: information encoded within the weights of a model during training. It’s what shapes how a model understands the world, recognizes entities, and forms default answers.

Fancy technical term for real marketing consequences. Because on the marketing side of things, when a machine knows who you are without having to look you up, that’s recall doing its job.

The catch is that you don’t get to decide when that happens. Training data is updated in cycles, and the window for getting into the next one doesn’t stay open indefinitely.

This is the long game, and it’s a little bit of the wild west right now. A few days ago, I would have said prepare for that window to open once a year. Yesterday, I was thinking maybe it’s a semi-annual cadence gap. Today, I’m thinking, “Yeah… probably annual… or something.” Just being transparent here.

I typically hedge by saying you’re trying to get into a training corpus a few cycles away: not this one, the next one, maybe the one after that. That hedge just got tighter.

The same research found that one major training dataset (RedPajama) behaved as if its knowledge stopped in 2019, even though it included a 2023 Wikipedia dump. More than 80% of its Wikipedia documents turned out to be older versions, and deduplication pipelines missed many of the extra copies. Old content stuck around. (Cheng et al., “Dated Data,” 2024)

In a nutshell, the long game may be longer than I originally thought. Which makes starting now more urgent, not less.

We don’t get to choose what gets encoded in machines. AI companies don’t publish the secret sauce behind their sourcing criteria. What we can observe, from patterns and from what AI systems treat as authoritative, is that training data skews toward sources seen as stable and credible. Academic research. Large publications with significant subscriber bases. Research firms. Content that gets cited repeatedly, attributed clearly, across multiple sources over time.

Your play isn’t to aim directly at the training data. Your play is to show up in the kinds of places training data pulls from, consistently enough and attributed clearly enough, that when the next cycle runs there’s a reasonable probability your ideas get encoded. Or show up alongside those sources.

Earlier this year, I published a book that probably could have been a year-long LinkedIn newsletter series. Same content. Same thinking. I published it as a book specifically to increase the likelihood of getting it into a training corpus sooner. I built a Notion tool around the same concept for the same reason: more surfaces, more signals, better odds. That’s the training data play. You’re not controlling the outcome. You’re managing the probability.

LLM behavior 2: Retrieval, what the machine goes out to find

Retrieval is the real-time layer. It’s what’s actively running when someone asks a question and the machine goes out to the web, or a connected knowledge base (think Project files), to find a current answer. This is the layer the market is almost entirely focused on right now, and for good reason. It’s responsive. It has a fast feedback loop. Publish something today and a retrieval-first system like Perplexity or Gemini can surface it within days.

Optimizing for retrieval means being crawlable, being findable, being structured in a way machines can parse. It means your identity is understood (a canonical bio can work wonders here), your site’s pages aren’t blocked, your content isn’t buried in JavaScript, and your metadata is doing signal work even when a machine decides not to read the full page.

One thing most retrieval-focused content skips: this layer is also responsible for personalized answers. That part is so important to remember.

Firecrawl’s breakdown of training data vs. retrieved data vs. live web data explains retrieval-augmented generation (RAG) as the system that gives AI agents access to specific, dynamic, or context-aware knowledge at the moment of a query.

In plain language: when someone asks an AI system to recommend something and the answer feels tailored to them specifically, that’s the retrieval layer working from what it knows about that user’s context.

“Best of” and “recommend me” queries aren’t just about who has the most content. They’re about who has the most relevant signal for that specific user in that specific moment.

That’s the generative in generative AI, by the way.

For brands and marketers: do the retrieval work. But know it’s one system, not the whole strategy. It may be a big part of your strategy, especially if you work in a fast-moving industry where the snapshot of the world encoded in training data hasn’t caught up to present reality. But it’s still only part of it.

LLM behavior 3: Inference, what the machine constructs when it decides it already knows

This is the one I haven’t seen many people building for. And it’s the one most likely to get you misrepresented.

Inference, in the developer sense, just means the model generating a response. It’s a mechanical term for what the model does. But there’s a specific visibility failure that lives inside inference that has no name in the brand strategy space yet: the moment when a machine decides it probably already knows enough about you or your topic to answer without retrieving anything at all.

It doesn’t go to your page. It doesn’t throw a “fetch tool failed” error. It assembles an answer from what it’s already holding (its training data, the context of the conversation, what it can see from a distance) and delivers it with full confidence.

A few weeks ago, I built a page specifically for machines: an onboarding page so I could introduce myself to a client’s AI system. Every other system could pull it. ChatGPT wouldn’t. When I asked why, it told me it could see the page just fine. It just wasn’t parsing it. It could infer.

It inferred completely wrong. Built a detailed, confident, entirely incorrect summary. No error message. No caveat. Just wrong, and indistinguishable from right.

This is what people mean when they say AI hallucinates. But hallucination makes it sound random. It wasn’t random. The machine made a probability judgment: I probably know this. It didn’t. And that’s a problem for your visibility strategy, because this is very likely happening to people pulling information about you, or about the thing you’re known for. And there’s no dashboard alert that says, “Hey, your dream client just pulled you up, but everything they saw was totally wrong.”

Constructing an inference strategy

It’s worth understanding what’s probably happening when a machine infers. First of all, it’s not personal, so I owe the team over at OpenAI an apology for repeatedly calling ChatGPT obstinate. And I’m by no means an engineer. I’m a writer who’s great at spotting patterns, and not afraid to have an entire conversation with machines about what the heck is happening at any given moment.

Inference ticks a couple of boxes at once. First, it requires fewer machine resources than retrieval. Second, it’s a risk-containment move. The machine is filled to the brim with data that’s been cleaned, filtered, deduplicated, and scored.

Your content hasn’t.

So if the machine comes across a query it decides it can answer without retrieving more information, it often will. And if it’s asked to go to a webpage that doesn’t convince it there’s something on that page it needs (from the URL, the context of the site, its trust and authority, and its schema), it may simply not go. It infers what the page is about from the URL structure and what the site is about. To the machine, that’s less risky than relying on content it hasn’t vetted.

The machine I’ve seen lean hardest into this behavior is ChatGPT.

You can’t reliably stop a machine from inferring. What you can do is construct the mirror it’s likely to use when it decides not to look directly. That means making sure your most important concepts, frameworks, and capabilities exist accurately across enough external surfaces that when the machine infers, it’s working from a reflection you built intentionally, not whatever it assembled from incomplete signals.

That includes:

• Your metadata
• Your social presence
• Your earned media
• Your citations
• The corroborating signals on third-party sites that tell the machine what it’s looking at before it decides whether to look closer

Build a strategy that covers all three

Training data. Retrieval. Inference.

Most visibility strategies are built for one. Maybe two. The brands that hold up across platforms, across time, and across the gap between what machines know now and what they’ll encode in the next training cycle are built for all three.

Increase the probability that whenever machines look, what they find out about you is accurate.

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