What are LLMs?
LLMs are not a new search engine. They are a lossy compression layer sitting on top of the same messy, biased, commercial web we have all been optimizing for the last 20 years, plus a bunch of proprietary data you will never see. Their job is not to “rank” your content. Their job is to synthesize patterns from whatever they are fed and return something that looks coherent and useful to a human.
LLMs are not using your SEO playbook
Most of the industry is still treating LLMs like “Google but chattier.” That is a category error. These systems do not have an E‑E‑A‑T knob. They do not reward your perfect H2 hierarchy or your 8th‑grade reading level. They consume token streams. They happily ingest PDFs, code comments, forum rants, transcripts, and mangled HTML and compress all of it into parameters.
What does LLM visibility mean?
For real‑time answers, tools like ChatGPT, Perplexity, or Gemini lean on search systems and APIs that look a lot like Bing/Google under the hood. That means your visibility is still bottlenecked by classic retrieval: if those engines do not surface you, the LLM never even gets the chance to see you in the first place. For “frozen” knowledge (model weights, cached corpora, proprietary datasets), you do not control the crawl, the sampling, or the update cycle at all.
Query fan‑out is the real problem
When you test your brand in Google, you see one query and one set of results. When a user types a similar question into an AI answer box, that is not what actually happens. LLM search fans your prompt out into a cluster of related queries, runs those in the background, and assembles an answer from whatever comes back.
So you think “we rank #1 for our core query, why are we invisible in AI answers?” Because your entire strategy is built around one neat keyword and the LLM is hitting ten different messy, long‑tail, comparison‑shaped queries you never bothered to optimize for. In other words: you are not being punished in LLM land, you are being out‑retrieved by your competitors on the expanded query set.
Reverse engineering the Query Fan Out is the real answer to AI Visibility.
This is why people see their brand dominate Google for its own name, yet vanish when they ask “who are the top tools for X?” or “what are alternatives to Y?” The fan‑out shifts the playing field from “can you rank for one trophy keyword?” to “do you show up across the whole question cluster that emerges when people try to actually solve the problem?”
What LLMs actually pull from
When an LLM answers, three broad data sources are in play:
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Its frozen training data and internal weights
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Whatever live data it can fetch through search or APIs
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Any private or first‑party data connected by the user or platform
You do not get to submit your site into the first bucket. You barely see the second bucket, except indirectly through SERPs and citations. The third bucket belongs to whoever controls the user relationship (the SaaS platform, the enterprise deployment, etc.), not to you.
Where do websites and brands sneak in? Primarily through the same routes that already worked for Google:
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Pages that rank for commercial and informational queries
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Content that is heavily linked, referenced, or embedded in other visible properties
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Entities that are consistently mentioned across different sites and content types
That is why Reddit, YouTube, Wikipedia, vendor docs, and big SaaS blogs suddenly feel omnipresent in AI responses. They already owned a huge share of the query fan‑out. The LLM did not “decide” they are authoritative. It inherited that bias from the retrieval stack.
Idiotic myths that refuse to die
Because this is SEO, the myths arrived before the measurements. A few favorites:
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“LLMs prefer fresh content.” Models trained on snapshots of the web cannot prefer something that did not exist when they were trained. Freshness is a retrieval bias, not a mystical love of newness.
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“Write in an AI‑friendly style with super‑clear headers and you’ll get cited.” LLMs are not grading your blog format. They see text, not your pretty design system. Clear structure helps search engines and humans, but it is not a magic LLM‑ranking switch.
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“Optimize your content for one tool (Perplexity / ChatGPT) and it will generalize.” Each product has different integrations, update cycles, and guardrails. You can track patterns, but there is no universal “LLM SEO checklist” that unlocks all of them at once.
These myths are attractive because they turn an uncomfortable truth (“you are not visible enough across the web”) into a comforting tactic (“just rewrite everything and add more FAQs”). But you cannot AB‑test your way out of a visibility gap if the retrieval layer never sees you.
So what actually works?
If you strip away the hype, LLM “optimization” looks very familiar:
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Win more surface area in search. Not just trophy terms, but the long‑tail, the comparisons, the “X vs Y”, the “best for Z”, the “how do I” questions that show up in fan‑outs.
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Build real entity presence. Make your brand, people, and products show up across different sites and formats. Mentions matter, not just links.
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Create content that is easy to quote. Clear, declarative statements, strong definitions, obvious “this is the answer” sections. Humans like them, and LLMs reach for them when they need to sound confident.
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Attach yourself to the sources LLMs love. That means being present on platforms that already dominate SERPs: Reddit, YouTube, developer docs, high‑authority blogs, niche communities. Not with spam, but with genuine, high‑signal contributions.
The uncomfortable part is that this is slower and harder than tweaking metadata. The good news is that if you are already good at SEO and content, you are not learning an entirely new discipline. You are just playing on a slightly different board.
The mindset shift SEOs need
Stop asking “how do I rank in LLMs?” and start asking:
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“Where does the retrieval stack get its evidence when people ask real questions about my space?”
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“When query fan‑out explodes my core keyword into a dozen variants, am I still there, or do I vanish?”
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“If an LLM had to explain my product to a stranger, what pieces of content across the web would it lean on—and do I own any of them?”
