GEO, LLMO, AEO… It’s All Just SEO

As a marketer, I want to know if there are specific things I should do to improve our LLM visibility that I am not currently doing as part of my routine marketing and SEO efforts.

So far, it doesn’t seem like it.

There seems to be massive overlap in SEO and GEO, such that it doesn’t seem useful to consider them distinct processes.

The things that contribute to good visibility in search engines also contribute to good visibility in LLMs. GEO seems to be a byproduct of SEO, something that doesn’t require dedicated or separate effort. If you want to increase your presence in LLM output, hire an SEO.

Sidenote.

GEO is “generative engine optimization”, LLMO is “large language model optimization”, AEO is “answer engine optimization”. Three names for the same idea.

recent LinkedIn posts:

“There’s only so many ways you can shake a stick at aggregating a group of information, ranking it, and then disseminating your best approximation of what the best and most accurate result/info would be.”

LinkedIn post and received some truly excellent responses.

Most people agreed with my sentiment, but others shared nuances between LLMs and search engines that are worth understanding—even if they don’t (in my opinion) warrant creating the new discipline of GEO:

PageRank).

LLMs operate differently. They derive their understanding of a brand’s authority from words on the page, from the prevalence of particular words, the co-occurrence of different terms and topics, and the context in which those words are used. Unlinked content will further an LLM’s understanding of your brand in a way that won’t help a search engine.

As Gianluca Fiorelli writes in his excellent article:

“Brand mentions now matter not because they increase ‘authority’ directly but because they strengthen the position of the brand as an entity within the broader semantic network.
When a brand is mentioned across multiple (trusted) sources:
The entity embedding for the brand becomes stronger.
The brand becomes more tightly connected to related entities.
The cosine similarity between the brand and related concepts increases.
The LLM ‘learn’ that this brand is relevant and authoritative within that topic space.”

Many companies already value off-site mentions, albeit with the caveat that those mentions should be linked (and dofollow). Now, I can imagine brands relaxing their definition of a “good” off-site mention, and being happier with unlinked mentions in platforms that pass little traditional search benefit.

As Eli Schwartz puts it,

“In this paradigm, links don’t need to be hyperlinked (LLMs read content) or restricted to traditional websites. Mentions in credible publications or discussions sparked on professional networks (hello, knowledge bases and forums) all enhance visibility within this framework.”

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site reputation abuse).

These tactics offer enough SEO benefit that many people still deem them worthwhile, but they will offer even less benefit for LLM visibility. Without any relevant context surrounding these links or articles, they will do nothing to further an LLM’s understanding of the brand or boost the likelihood of it appearing in outputs.

Andrej Karpathy points out, there may be a growing benefit to writing documents that are structured first and foremost for LLMs, and left relatively inaccessible to people:

“It’s 2025 and most content is still written for humans instead of LLMs. 99.9% of attention is about to be LLM attention, not human attention.
E.g. 99% of libraries still have docs that basically render to some pretty .html static pages assuming a human will click through them. In 2025 the docs should be a single your_project.md text file that is intended to go into the context window of an LLM.
Repeat for everything.”

This is an inversion of the SEO adage that we should write for humans, not robots: there may be a benefit to focusing our energy on making information accessible to robots, and relying on the LLMs to render the information into more accessible forms for users.

In this way, there are specific information structures that can help LLMs correctly understand the information we provide.

For example, Snowflake refers to the idea of “global document context”. (H/T to Victor Pan from HubSpot for sharing this article.)

LLMs work by breaking text into “chunks”; by adding extra information about the document throughout the text (like company name and filing date for financial text), it’s easier for the LLM to understand and correctly interpret each isolated chunk, “boosting QA accuracy from around 50%-60% to the 72%-75% range.”