LLM SEO: The Two Levers That Decide What AI Says About You

LLM SEO is the practice of making your brand easy for large language models to find, understand, and cite. You will also see it called LLM optimization, LLMO, GEO, or AEO. The labels matter less than one structural fact most guides skip: every AI answer about your brand draws on two separate mechanisms, and they respond to completely different work.

The first is what the model learned during training. The second is what it retrieves live when someone asks a question. Treat them as one thing and you end up doing slow work where fast work was available, or waiting for results that were never going to come from the lever you pulled.

What people mean by LLM SEO

The naming is genuinely unsettled. Search Engine Land has surveyed practitioners and found they cannot agree on a single term, and “what is AI SEO called now” is itself a question people type into Google. Here is how the labels map:

We break the differences down further in AEO vs GEO vs SEO and define the discipline in what is GEO. For this guide, LLM SEO means one thing: influencing what a language model says about you.

The two levers behind every AI answer

Lever 1: training data

A language model’s built-in knowledge comes from pretraining on very large text collections, much of it crawled from the public web. OpenAI’s GPT-3 paper, for example, reported that filtered Common Crawl data made up 60% of its training mix by weight, with curated sources such as WebText, books, and Wikipedia making up the rest. Newer models use larger and more carefully filtered mixes, and most labs no longer publish exact recipes, but the principle holds: the public web, weighted toward sources judged high quality, becomes the model’s memory.

That memory is often called parametric knowledge, because it lives in the model’s parameters rather than in any document it can point to. Three properties follow.

• It is frozen at a cutoff. Anything published after the training data was collected does not exist in the model’s memory.
• It cannot cite. The model does not know which page taught it a fact, so memory-based answers arrive without sources.
• It is a consensus, not a record. A fact repeated consistently across many sources is learned firmly. A fact that appears once, or contradicts itself across sources, is learned weakly or not at all.

What you can influence in training data

• Crawl eligibility. Training crawlers such as GPTBot, Google-Extended, and Common Crawl’s CCBot can only collect pages they are allowed to fetch. Whether to allow them is a business decision we work through in should you block AI crawlers.
• Consistency across widely copied sources. Wikipedia, Wikidata, major directories, and reputable publications are heavily represented in training data and copied across the web. A consistent description of you in those places is what the model absorbs. See Wikidata for brands and the entity SEO guide.
• Volume of accurate, independent mentions over time. This is slow, compounding work, which is why it overlaps so heavily with digital PR for AI search.

What you cannot influence

You cannot submit content for training, you cannot edit what a model already learned, and you cannot choose when the next model is trained. Work on this lever pays off only when a future model version is trained on data that includes it.

Lever 2: live retrieval

Search-enabled assistants do not rely on memory alone. When ChatGPT search, Perplexity, Gemini, or Google’s AI Mode answers a question, it queries a search index, fetches candidate pages, and writes an answer grounded in what it retrieved. That pattern is retrieval-augmented generation, and it is where citations come from.

Retrieval runs on infrastructure you can see and influence:

• Search indexes. ChatGPT’s search leans heavily on Bing’s index. A Search Engine Land case study published in April 2026 found that ranking in Bing aligned with ChatGPT brand mentions far more closely than ranking in Google did. Google’s AI features use Google’s own index.
• Retrieval crawlers. OAI-SearchBot indexes pages for ChatGPT search, and ChatGPT-User fetches a page when a user asks about it directly. PerplexityBot indexes for Perplexity. These are separate from the training crawlers above.
• The selection step. Being fetched is not being quoted. An AirOps analysis of 548,534 pages retrieved across 15,000 ChatGPT prompts, reported by Search Engine Land in March 2026, found only 15% of retrieved pages were cited in the final answer. We unpack that gap in retrieval vs citation.

What you can influence in retrieval

Almost all of it, and quickly: indexation in both Google and Bing, crawler access, server-side rendering, passage structure, freshness, and on-page facts that match what independent sources say. Changes here can show up in answers within days to weeks.

How the two levers compare

The levers also interact. When an assistant searches the web, fresh retrieved facts usually override what the model remembers. When it does not search, which still happens for many conversational prompts, it falls back on memory. That is why an outdated fact can persist in some answers long after you fixed it on your site: the retrieval layer has the new version, the parametric layer still holds the old one. We cover how to diagnose and correct that in when AI gets your brand wrong.

Is LLM SEO just SEO? The honest split

The top discussions for this term include a thread calling LLM SEO a scam, and a well-watched talk arguing it is “just SEO, mostly.” Both have a point. Most of the work is ordinary SEO done properly. A smaller part is genuinely new, and that part is where teams that only do classic SEO fall behind.

If an agency sells LLM SEO without touching the left column, it is selling the garnish without the meal. If it only does the left column, it is classic SEO under a new invoice line. The useful version does both, and it measures the right-hand column so you can see whether it worked.

An LLM SEO checklist in ten moves

1. Confirm access. Check robots.txt and your CDN or firewall rules for every retrieval crawler you want citing you.
2. Get indexed in Bing as well as Google. Submit your sitemap to Bing Webmaster Tools and use IndexNow to push new URLs.
3. Render content on the server. Many AI crawlers do not execute JavaScript.
4. Write answer-first passages. Lead each section with the claim, then support it.
5. State your core facts identically everywhere. Name, category, location, founding year, founders, pricing model.
6. Connect your entity with structured data. One Organization node with a stable @id and a short, genuine sameAs list.
7. Earn independent mentions. Reviews, directories, publications, and category roundups.
8. Publish something only you have. Original numbers, first-hand process detail, real screenshots. Models already hold the generic version of every topic; retrieval systems go looking for the specific one.
9. Refresh on a schedule, honestly. Revisit the pages that earn citations every quarter, change what has actually changed, and let dateModified move only then.
10. Measure both levers separately. Run your prompt set with web search on and off, and log which answers cite sources.

For the full version, work through our GEO checklist and the technical SEO checklist for AI search. If the vocabulary is new, the AI search glossary defines every term used here.

Where LLM SEO fits in an AI SEO program

LLM SEO is not a separate channel. It is the recognition that AI answers have two supply lines and that each needs different work. Retrieval is the lever to pull first because it is fast, measurable, and where commercial answers are grounded. Training data is the lever you keep working in the background, so that the model’s memory and the live web tell the same story about you.

If you want both levers assessed for your brand, with a prompt set run across the major engines, our AI SEO service starts there.