🔍 SEO / GEO

Large Language Model Optimization

llmo · llm optimization · llm optimisation · llm seo · large language model optimization · ai brand visibility

Definition

Large language model optimization is the set of measures that raise the probability of a brand being mentioned and its content cited by large language models, through presence in authoritative sources, structured data and llms.txt.

Why LLMO matters now

Before 2023 people looked for information through search engines and clicked through to sites. With the spread of ChatGPT, Perplexity, Claude and AI Overviews, a share of those queries now gets an answer inside the interface, with no visit to any site at all.

For brands that raises a new question. Not what position do we hold in Google, but does the model know about our product and does it recommend us.

There are two mechanisms through which an LLM knows about a brand:

Parametric knowledge — information encoded in the model’s weights during training. You can only influence it indirectly, through presence on platforms that end up in the training corpora.
Retrieval (RAG) — at answer time the model looks for current sources. This is where SEO and GEO tactics apply.

LLMO tactics

Presence on authoritative platforms

Models are trained on web data, with a bias towards authoritative sources. To reach parametric knowledge:

Wikipedia — an article about the brand or the technology
Technical communities and trade publications — technical posts with concrete facts
Press coverage — mentions in business and industry media
Structured databases — Wikidata, Schema.org markup on the site

Content for retrieval

To be picked up by the retrieval components of AI search engines such as Perplexity and AI Overviews:

Tactic	Principle
Answers to specific questions	A question-and-answer structure in the H2 and H3 headings
Facts and figures	Models cite sources with concrete data far more often than abstract text
Freshness	Update publications regularly — recent content is preferred
Schema.org markup	Makes structured facts easier to extract

Important: GEO and LLMO do not replace SEO, they extend it. Most of the signals that help a page rank in Google — authority, quality content, a solid link profile — work for LLMO too. Content written well for people is usually cited well by models.

Monitoring results

LLMO is harder to measure than classical SEO. The workable approaches:

Manual checks — regularly ask the key questions of your niche in ChatGPT, Claude and Perplexity: which personalization platforms exist for e-commerce, compare X and Y, the top tools for A/B testing. Record whether the brand is mentioned and how.
AI referral traffic — in Google Analytics 4, isolate sessions with the sources perplexity.ai, chatgpt.com and copilot.microsoft.com. Growth in that traffic is an indicator of citability.
Dedicated tools — Profound, Otterly.ai and similar services track brand mentions in LLM answers automatically; the tooling market is still taking shape.

Frequently asked questions

How does LLMO differ from GEO?

Generative engine optimization focuses on the retrieval component of AI search engines such as Perplexity and AI Overviews, which pull documents and generate an answer from them. LLMO is broader: it also addresses the parametric knowledge inside the model, acquired through training data, and applies to every LLM-driven system rather than search alone.

Can you get into an LLM's training data directly?

No — you do not control which data a given model uses at its next training run. The indirect route is to publish on platforms that belong to the standard corpora: Wikipedia, major news resources, well-known technical communities, GitHub. Recent events reach models through retrieval components or retraining, with a lag of several months to a year.

What is llms.txt and why does it exist?

llms.txt is a text file at the root of a site, analogous to robots.txt, describing what the resource contains in a format convenient for LLMs: key sections, structure, important pages. It helps LLM agents orient themselves in a site faster. The standard is still forming but is already supported by a number of AI platforms.

How measurable is LLMO?

Less measurable than SEO. Tools for monitoring mentions in LLM answers are still maturing. The current approaches are manual prompts in ChatGPT, Claude and Perplexity on the key questions of your niche, tracking through dedicated services such as Profound or Otterly.ai, and analysing referral traffic from AI search engines in Google Analytics.