aeo
LLM SEO vs SEO: What Actually Changes.
The LLM SEO vs SEO question usually shows up in a marketing leadership meeting right after someone notices AI referral traffic in analytics for the first time. The short answer: LLM SEO is the practice of getting a large language model to name and recommend your company inside a generated answer, while SEO is the practice of getting a page to rank in a list of links. They share some inputs but optimise for a different outcome, and treating them as the same discipline is why most B2B sites still show close to no AI citations despite years of SEO investment.
This matters because the two disciplines are converging in budget conversations before most teams have separated them in practice. If you are the person who has to decide where the next quarter of content and technical work goes, you need a clear answer to LLM SEO vs SEO that is not just vibes.
What is LLM SEO?
LLM SEO is the set of practices that make a brand more likely to be named, quoted, or recommended when someone asks ChatGPT, Perplexity, Gemini, or Copilot a question relevant to that brand's category. It is close to what the industry also calls answer engine optimisation (AEO) or generative engine optimisation (GEO): the terms overlap heavily and are often used interchangeably by buyers, which is part of why the category is confusing to search right now.
Unlike SEO, LLM SEO has no fixed results page to inspect. The model composes an answer at query time, drawing on training data, live retrieval, and whatever sources it judges most trustworthy for that specific question. There is no static SERP to check rank position against, which is the first practical difference a marketing team runs into.
How is LLM SEO different from traditional SEO?
The core difference is the unit being ranked. SEO ranks a URL against other URLs for a query. LLM SEO evaluates an entity (your company, product, or expert) against other entities for a claim the model is willing to make in an answer. A page can rank well in Google and still never get cited by an AI engine, because the model is not deciding whether to list the page, it is deciding whether to trust the entity behind it enough to name it.
That shift changes what "good content" means. SEO rewards a page that satisfies search intent well enough to hold a position. LLM SEO rewards a page (and the entity behind it) that supplies a clean, quotable, well-corroborated claim the model can lift with confidence. Structured data, consistent entity naming across the web, and third-party corroboration matter more in LLM SEO than they typically do for ranking a page in classic SEO.
| Dimension | Traditional SEO | LLM SEO |
|---|---|---|
| Unit ranked | A URL | An entity (company, product, person) |
| Result surface | A list of ten blue links | One generated, synthesised answer |
| Primary signal | Backlinks, on-page relevance, page speed | Entity consistency, structured data, third-party corroboration |
| Content that wins | Comprehensive pages that satisfy intent | Direct, quotable claims an engine can lift with confidence |
| Where you check performance | Rank tracker against a keyword | Repeated prompt testing against a tracked query set |
| Update cadence that matters | Ongoing, incremental | Freshness spikes matter more for live-retrieval engines |
| Failure mode | Page 2, no clicks | Page ranks fine, entity still never gets named |
Gartner has estimated that traditional search engine volume could fall by around 25 percent by 2026 as this kind of answer-first behaviour spreads, and BrightEdge data puts the share of searches that now trigger an AI Overview at roughly 58 percent. Both numbers point the same way: the "ten blue links" surface is shrinking as a share of total attention, even where it still exists.
Does LLM SEO replace SEO or work alongside it?
It works alongside it, not instead of it. Most of the technical foundation of SEO (crawlable HTML, fast pages, clear information architecture, and legitimate backlinks) is also the foundation LLM SEO depends on, because AI engines still need to be able to crawl, parse, and trust a page before they can cite it. Teams that frame this as SEO vs LLM SEO and pick one are choosing between two systems that are supposed to run on the same underlying site health.
Where they diverge is in what gets added on top. SEO stops at getting the page found and clicked. LLM SEO adds work that SEO never required: disambiguating your entity from similarly named competitors, publishing content in a format models can quote directly, and building the off-site citations (directories, industry write-ups, structured profiles) that corroborate your claims outside your own domain. You can read a direct breakdown of the two disciplines on our AEO vs SEO explainer.
Which SEO fundamentals still carry over to LLM SEO?
Three carry over almost unchanged: crawlability, page speed, and topical depth. If a page cannot be crawled or parsed cleanly, no AI engine can cite it either, so basic technical SEO hygiene is not optional groundwork you can skip once you decide to invest in LLM SEO.
Topical depth also carries over because both systems reward genuine authority over thin content. A site with a shallow, scattered content footprint rarely gets cited by an AI engine for the same reason it rarely ranks well in classic SEO: neither system can locate consistent expertise to reward.
What does LLM SEO add that SEO does not cover?
LLM SEO adds entity work that SEO has no equivalent for: consistent naming and description of your company across your site, schema markup, and third-party sources, so a model can resolve "who is this" without ambiguity. It also adds off-site citation building aimed specifically at the sources models retrieve from, which is a different link-building target list than the one SEO teams typically prioritise.
It also adds a different content shape. SEO content is often written to be comprehensive so it can rank for a cluster of related keywords on one page. LLM SEO content performs better when individual claims are stated in a single, self-contained sentence a model can lift without needing surrounding context, because that is literally how the model extracts and quotes source material. SourceRank AI's own structured data guide covers the schema side of this in more depth.
How do you measure results differently for LLM SEO vs SEO?
You measure SEO with rank position and organic clicks; you measure LLM SEO by repeatedly prompting the target engines with a fixed query set and recording whether, and how, your brand gets named. A rank tracker cannot answer whether ChatGPT recommends you, because there is no rank position to track. SourceRank AI audit data across B2B categories shows the average company is cited in fewer than 5 percent of the prompts most relevant to its own category, which is the baseline most teams are starting from whether they have measured it or not.
This is also where generic "AI rank tracker" tools can mislead a team: a single prompt run is a snapshot, not a measurement, because model answers vary between runs and between engines. A defensible LLM SEO measurement programme tracks a representative prompt set repeatedly over time, the same way our AEO score is built.
Where should a B2B team start on LLM SEO?
Start by measuring, not by writing content. Follow this sequence:
- 1. Get a baseline. Run a free AEO score to see your current citation rate across ChatGPT, Perplexity, Gemini, and Copilot before changing anything.
- 2. Map the fan-out. List the sub-questions buyers and AI engines expand your core queries into, not just the head keyword.
- 3. Fix entity signals. Standardise your company name, description, and schema markup across your own site first.
- 4. Build off-site corroboration. Secure citations in the directories, roundups, and third-party sources the engines actually retrieve from.
- 5. Re-test on a fixed cadence. Compare against your baseline monthly, not once, since model behaviour shifts between updates.
Our how it works page walks through this sequence in more detail, and pricing covers what it costs to run.
Frequently asked questions
What is LLM SEO?
LLM SEO is the practice of increasing the odds that a large language model names, quotes, or recommends your company when answering a question in your category, as opposed to classic SEO which aims to rank a page in a list of search results.
Is LLM SEO the same as AEO?
Largely yes. LLM SEO, answer engine optimisation (AEO), and generative engine optimisation (GEO) describe overlapping practices aimed at AI-generated answers, and the terms are used close to interchangeably in the market today, though AEO is the more established name for the discipline.
Should I stop doing SEO if I invest in LLM SEO?
No. LLM SEO depends on the same technical foundation SEO requires, including crawlable pages, fast load times, and genuine topical authority, so dropping SEO investment would undermine the LLM SEO work rather than fund it.
Which AI engines does LLM SEO target?
The main targets are ChatGPT, Perplexity, Gemini, and Microsoft Copilot, since these are the engines B2B buyers most commonly use for research, and each weighs entity signals and retrieval differently.
How long does LLM SEO take to show results?
Entity and schema fixes can show up in citation testing within weeks for engines with live retrieval like Perplexity, while engines with fixed training cutoffs move more slowly, so most programmes plan for a multi-month horizon.
Can a small B2B team run LLM SEO without an agency?
Yes, provided the team can commit to measurement discipline (a fixed prompt set, tested repeatedly) and has the technical access to fix schema and entity signals; teams without that bandwidth typically hire this out. Our services page outlines what a managed programme covers, and contact us if you want a second opinion on which route fits your team.
What is the single biggest difference between SEO and LLM SEO in practice?
SEO teams optimise pages; LLM SEO requires optimising the entity behind the pages, including how it is described and corroborated everywhere else on the web, not just on the company's own domain.
How do you measure LLM SEO ROI?
Track citation rate against a fixed prompt set over time, alongside any AI-referral traffic that shows up in analytics, and treat rank position as a separate, parallel metric rather than a proxy for AI citation.