LLM SEO is the practice of getting your brand cited or recommended when someone asks a large language model (ChatGPT, Perplexity, Gemini, Copilot) a question. It works through two mechanisms: the model's training data (how often and how favourably you're mentioned across the web it learned from) and live retrieval (which pages the model's search layer pulls in and cites in real time). It's closely related to answer engine optimization, of which it's essentially the LLM-specific part.

How do you get cited by ChatGPT?

Two paths. To be recommended from the model's own knowledge, you need consistent mentions across the trusted sources it trained on, round-ups, comparison posts, "best X" lists, reputable directories, references in respected publications. To be cited when ChatGPT does a live search, you need to rank well for the query and have a page the model can extract a clean answer from (direct answer near the top, clear structure, schema). Doing both, mention density plus a well-ranked, extractable page, is how you show up.

How is LLM SEO different from regular SEO?

Regular SEO is about ranking a page in the list of results. LLM SEO is about being one of the brands or sources a model mentions in a synthesized answer. The signals overlap heavily, you generally need to rank to be cited, but LLM SEO adds emphasis on brand-mention density across trusted sources, factual consistency about your brand everywhere it appears, and content structured so a model can extract from it. It's SEO plus a digital-PR layer, aimed at being in the answer rather than in the list.

Can you optimize for Perplexity specifically?

Yes, and it's one of the more tractable ones, because Perplexity does heavy live retrieval and cites its sources prominently in the answer. That means a page that ranks well for the query and is structured for easy extraction has a real shot at being a cited source. The work is the same as the general LLM SEO playbook, rank, structure for extraction, build authority and mentions, with the encouraging note that Perplexity's transparency about its sources makes the cause and effect easier to see.

LLM SEO: How to Get Cited by ChatGPT, Perplexity and Other AI Models in 2026

LLM SEO is the work of getting your brand cited or recommended when someone asks a large language model a question, ChatGPT, Perplexity, Gemini, Copilot. It overlaps with classic SEO and with answer engine optimization, but it has its own logic, because an LLM doesn't "rank" pages so much as decide which sources and brands to mention in a synthesized answer. This guide covers how that decision gets made, the playbook for influencing it, and how it differs from the SEO you already know.

For the broader context, see answer engine optimization (LLM SEO is essentially the LLM-specific slice of AEO) and AI visibility for agencies.

How LLMs decide what to cite and recommend

Two mechanisms, working together:

Training data. What the model "knows" about your brand comes from how often and how favourably you're mentioned across the web the model was trained on. This is powerful and slow: you can't change last year's training data, but you can influence what goes into the next cut. Brand-mention density and consistency across trusted sources is the foundation here.
Retrieval (live search). ChatGPT with search, Perplexity, and Google's AI all do live web searches and cite the pages they pull in. For these, the question is which pages the system retrieves and lifts from, which depends on classic ranking signals plus how extractable your page is. This part you can influence quickly.

A useful way to think about it: training data decides whether the model recommends you off the top of its head; retrieval decides whether it cites you when it goes and checks. You want both.

The LLM SEO playbook

1. Be mentioned consistently across the sources LLMs read

This is the single biggest factor. Round-ups, "best X" lists, comparison posts, reputable directories, references in respected publications, and yes, Wikipedia-adjacent pages where appropriate. When the same brand keeps showing up across the sources a model trained on (and across the pages it retrieves now), it becomes one of the names the model reaches for. This makes LLM SEO partly a digital-PR job: get included in the lists and comparisons that already exist, and create the ones that don't.

2. Be the source that's easy to cite

When an LLM does a live search, it lifts from pages it can extract a clean answer from. A direct answer near the top, clear headings phrased as questions, lists for steps and options, tables for comparisons, an FAQ block, and clean schema (FAQPage, Article). Pages that bury the answer under preamble, or that are unstructured walls of text, are harder to quote, so they get quoted less.

3. Rank well in classic search

The retrieval layer leans on existing rankings. ChatGPT search, Perplexity, and Google's AI all draw heavily from pages that already rank. LLM SEO is not a way around technical health, internal linking, and authority; it's a layer on top of a site that already works. If you can't rank for a query, you'll struggle to be cited in the answer for it.

4. Get the facts straight and consistent everywhere

LLMs aggregate, and conflicting information confuses them. Your product description, your category, your differentiators, your basic facts, should say the same thing on your site, your profiles, the directories you're in, and the articles that mention you. Inconsistent or outdated information about your brand across the web is noise the model has to resolve, and sometimes resolves wrong.

5. Cover the topic comprehensively

Models favour sources that demonstrably know the subject. A coherent cluster of interlinked pages on a topic beats a single thin page, the same way it does in classic search. Depth and breadth on a topic signal "this is a source worth citing on it."

6. Build genuine authority

Being referenced by sites the model trusts carries weight. This is the same authority work SEO has always involved, expressed through who cites and links to you, applied with an eye to the sources LLMs are likely to weight.

7. Don't try to game it

Hidden text aimed at LLMs, prompt-injection-style tricks in your content, fake reviews to inflate mentions, these are bad ideas. They're against the platforms' interests, they'll get patched, and they risk your reputation when discovered. LLM SEO that lasts is the same disciplined content, PR, and authority work as good SEO, aimed at a slightly different target.

Platform notes

ChatGPT: a mix of training-data knowledge and live search. To be recommended off the top, you need the mention density; to be cited when it searches, you need to rank and be extractable.
Perplexity: heavy live retrieval, with sources cited prominently in the answer. Being a cited source here is genuinely achievable for a well-ranking, well-structured page, more so than being in ChatGPT's "default" recommendations.
Google's AI Overviews: mostly pulls from pages already ranking on page one. This is the most "classic SEO adjacent" of the answer surfaces. (Tactics: how to rank in AI Overviews.)
Gemini and Copilot: similar logic to the above, with their own search backends. The playbook doesn't change much by platform; the weighting does.

How to measure it

You can't manage what you can't see. Run the queries that matter (your category, "best X," the questions your buyers ask) through the LLMs and check whether you're named, who's named instead, and which sources are cited. Do it on a schedule to see whether your work is moving the number. Tools and a worked explanation: AI visibility tools and what is an AI-visibility audit.

For agencies

LLM SEO is the content-and-PR side of the AI-visibility service. Audit where the client (or prospect) stands, reformat and structure the content, build the mentions, and report on the change. It's packageable as a service line and it's a strong differentiator, since most competitors aren't offering it yet. See how to offer AI-visibility audits as an agency service and becoming an AI SEO agency.

FAQ

Frequently asked questions

What is LLM SEO?: LLM SEO is the practice of getting your brand cited or recommended when someone asks a large language model (ChatGPT, Perplexity, Gemini, Copilot) a question. It works through two mechanisms: the model's training data (how often and how favourably you're mentioned across the web it learned from) and live retrieval (which pages the model's search layer pulls in and cites in real time). It's closely related to answer engine optimization, of which it's essentially the LLM-specific part.
How do you get cited by ChatGPT?: Two paths. To be recommended from the model's own knowledge, you need consistent mentions across the trusted sources it trained on, round-ups, comparison posts, "best X" lists, reputable directories, references in respected publications. To be cited when ChatGPT does a live search, you need to rank well for the query and have a page the model can extract a clean answer from (direct answer near the top, clear structure, schema). Doing both, mention density plus a well-ranked, extractable page, is how you show up.
How is LLM SEO different from regular SEO?: Regular SEO is about ranking a page in the list of results. LLM SEO is about being one of the brands or sources a model mentions in a synthesized answer. The signals overlap heavily, you generally need to rank to be cited, but LLM SEO adds emphasis on brand-mention density across trusted sources, factual consistency about your brand everywhere it appears, and content structured so a model can extract from it. It's SEO plus a digital-PR layer, aimed at being in the answer rather than in the list.
Can you optimize for Perplexity specifically?: Yes, and it's one of the more tractable ones, because Perplexity does heavy live retrieval and cites its sources prominently in the answer. That means a page that ranks well for the query and is structured for easy extraction has a real shot at being a cited source. The work is the same as the general LLM SEO playbook, rank, structure for extraction, build authority and mentions, with the encouraging note that Perplexity's transparency about its sources makes the cause and effect easier to see.