Blog · AI Search

Aug 2026 · 12 min read

LLM SEO: what the term means, and what is actually different

The name is unfortunate and the category is real. A working definition, how the major assistants differ from each other, and an honest account of what the work is worth right now.

Yash Tulsyani

Growth systems architect

A working definition, since the term is doing too much

LLM SEO, AI SEO, GEO, answer engine optimisation. Four names for roughly one idea, none of them good, all of them in circulation.

The useful definition is narrow: making your content likely to be retrieved and cited when someone asks an assistant a question your content answers. That is it. Everything else being sold under these names is either conventional SEO, ordinary content quality, or invention.

What makes it a distinct concern rather than a rebrand is the mechanism. Search ranks documents and a person chooses among them. Assistants retrieve passages and a model synthesises an answer, usually citing a handful of sources. The middle step is gone: nobody is choosing your result from a list, because there is no list.

That single difference produces every real change in the work. The rest of the discourse is downstream of it, and much of the discourse gets it wrong by treating the assistant as a search engine with a chat interface.

The mechanism itself, and what a retrieved chunk actually looks like, is set out in what AI search actually retrieves from a page.

The assistants are not interchangeable

Most advice treats AI search as one thing. The major systems differ enough in how they source answers that a single strategy is a simplification, and knowing the differences changes where effort goes.

How the main systems source an answer

System	Sourcing behaviour	What that rewards
ChatGPT with browsing	Searches, then reads a few results	Pages that rank conventionally and read cleanly in extract
Perplexity	Search-first, cites heavily and visibly	Being in the top conventional results, and being quotable
Google AI Overviews	Draws from its own index and ranking	Conventional SEO, almost entirely
Claude and similar, with search	Retrieves, weighs source quality	Depth and specificity over keyword coverage

Behaviour changes without notice. Treat this as a shape rather than a specification

The pattern across the table is the useful part: conventional ranking still feeds most of these systems. A page that cannot be found by a search engine will not be cited by an assistant that starts with a search, which is most of them. ChatGPT specifically is the clearest case of that split, since it answers either from a live search or from training, and only one of those is influenceable, as covered in the ChatGPT visibility piece.

That is the strongest argument against treating this as a separate programme. The foundation is the same foundation. What changes is what happens to your page after it is retrieved.

Perplexity specifically, because it is the clearest case

Perplexity is worth singling out because its behaviour is the most visible and the most instructive. It cites prominently, links out, and shows its sources, which makes it the easiest system to learn from.

What it rewards is close to conventional SEO with an extraction filter on top. It searches, takes a handful of results, and quotes from them. So ranking gets you considered and quotability gets you used.

Being in the top few conventional results still matters most. If you are not retrieved you cannot be cited, and its retrieval leans on search.
Short, self-contained factual statements get quoted. A sentence that states a fact completely is easier to lift than one that builds across a paragraph.
Recency is weighted more visibly than in conventional search. Updated pages appear more often on questions where currency plausibly matters.

It also sends meaningful referral traffic, which most assistants do not, so it is the one place where the effect of this work shows up in analytics at all. That makes it a useful proxy even if it is not your largest audience.

The caution with using it as a proxy is that its behaviour is the most search-like of the major systems, so improvements you see there may not transfer to assistants that retrieve differently. It is the best available signal and it is measuring the easiest case, which is worth holding in mind before treating a Perplexity gain as evidence that the whole approach is working.

What is genuinely new, in one list

Stripping out everything that is conventional SEO wearing a new name, four things are actually different.

The four real changes

The unit of optimisation is the section

Not the page. A section that only makes sense in context cannot be retrieved usefully, because it arrives at the model alone.

There is no partial credit

You are in the answer or you are not. No position four, no long tail of impressions accumulating while you climb.

Planning starts from questions, not keywords

Nobody types a keyword at an assistant. One well-answered question covers phrasings a keyword tool would list as separate terms.

Being wrong scales differently

An assistant that has learned something incorrect about your product repeats it consistently to everyone who asks, until its sources change. That is a new category of risk, not a new opportunity.

The last one gets the least attention and deserves more. Conventional search shows a user several results and lets them judge. An assistant gives one answer, and if that answer contains a wrong fact about your pricing or your capabilities, every person asking receives it.

The practical response to all four is covered in the AI search visibility guide, and the measurement problem in tracking brand mentions in AI search.

The claims worth ignoring

The field has filled with confident advice faster than it has filled with evidence. Four claims circulate widely and are either unsupported or actively wrong, and recognising them saves time.

Four things you will read that are not true

“Schema markup is how you get into AI answers”

Structured data helps a system parse what a page is and does not make a weak passage citable. It is worth having and it is not the lever. The claim persists because it is concrete and easy to sell.

“Traditional SEO is dead”

Most assistants start from a conventional search. A page that cannot be found by a search engine will not be cited by a system that begins by searching, which is most of them.

“You need to write for machines now”

The changes that help are self-contained sections, named subjects and claims stated before caveats. Those are legibility improvements a human reader benefits from equally. Nothing here rewards writing that reads badly.

“Here is the exact ranking factor list for ChatGPT”

Nobody outside these companies knows the weighting, the systems change without notice, and a specific factor list is a guess presented with false precision. Treat any numbered list of AI ranking factors as marketing.

The common thread is false precision. The honest state of knowledge here is that the direction is clear and the specifics are not, and advice that sounds more certain than that is selling something.

The test I would apply: does the recommendation still make the page better if the mechanism turns out to work differently? If yes, follow it. If it only pays off in one specific model of how retrieval works, it is a bet dressed as a best practice.

What it is worth right now, honestly

This is where most writing on the subject becomes marketing, so it is worth being specific about what is known and what is not.

What is known: assistant usage is growing, referral traffic from them is small but real, and the queries where they are used skew towards research rather than transaction. What is not known, by anyone, is what share of buying decisions they influence, because there is no measurement that would show it.

The honest state of the evidence

Growing

usage, consistently reported

Small

measurable referral traffic

Unknown

influence on decisions, and it may be large

Anyone presenting the third as a number is presenting a guess

Which produces a defensible position rather than an exciting one. The work is worth doing, because it costs editing time on content you have rather than a new programme, and because the changes it asks for are improvements regardless of who reads the page.

What is not defensible is reallocating meaningful budget away from channels with measurable return, on the strength of a trend nobody can size. The companies making that trade will mostly be wrong, and a few will be spectacularly right, which is the shape of every early bet.

The asymmetry is what makes it worth doing anyway: the downside of self-contained sections and specifically named entities is nothing, because those are better pages either way.

Who this changes things for, and who it does not

The urgency of this work is not evenly distributed, and treating it as universally pressing is how it gets over-invested by companies it will barely affect.

Where assistant visibility matters more, and less

Situation	How much it matters	Why
Technical or considered B2B purchase	A lot	Buyers research extensively and increasingly start by asking rather than searching
New or unfamiliar category	A lot	The assistant shapes which vendors are considered at all
Established brand, strong direct demand	Less	People search your name. The assistant is not choosing for them
Local or physical services	Little, so far	Assistants defer to maps and local listings for these
Impulse or low-consideration purchase	Little	Nobody consults an assistant before a small routine buy

The second row is where the asymmetry is largest

The second row is the one worth acting on quickly. In a category buyers do not yet have a mental list for, the assistant is effectively assembling the shortlist, and being absent from it is not a ranking problem but an existence problem.

The third row is a caution against panic. A company with strong branded demand is not at immediate risk from this, because the people buying already know who they are looking for. The risk there is slower and different: the next cohort of buyers may form their shortlist somewhere the brand has no presence.

Where this fits in an existing programme

Not as a separate workstream. The failure mode I would guard against is a company standing up an AI visibility initiative with its own owner, its own reporting and its own roadmap, running alongside the SEO programme that shares ninety percent of its inputs.

The realistic integration is three changes to work already happening.

How to absorb this without a new programme

Change the editing standard

Sections answer one question completely. Subjects named rather than referred to. Claims before caveats. Applied to everything new, and retrofitted to pages that already rank.

Add a monitoring question set

Twenty questions, run monthly. Forty minutes. It sits alongside rank tracking rather than replacing it.

Prioritise original data over synthesis

The highest-leverage change and the slowest. A number nobody else has is the strongest reason for any system to cite you.

Two of those are checkable rather than judged. The Entity Extractor reports whether a page establishes its subjects where it matters or merely repeats them, and whether a competing page names things yours does not. The Schema Validator catches the contradictory structured data that makes a page disagree with itself, which is worth ruling out before concluding the content is the problem.

None of that needs a new budget line, and all of it improves the conventional programme too, which is the test I would apply to any advice in this area. If a recommendation only makes sense in a world where assistants dominate, it is a bet. If it makes the pages better regardless, it is just work worth doing.

The organisational point underneath is worth stating separately, because it is the part that actually determines whether any of this happens. A separate AI visibility owner produces a separate roadmap, separate reporting and a quarterly document. The same person who owns content quality, adding three items to an existing standard, produces changed pages. The second costs nothing and is considerably harder to get agreement on, because it is not visible as an initiative.

If you want one thing to take from this piece, it is that. The work is real, the mechanism is different enough to matter, and the correct response is an editing standard rather than a programme. Everything sold as more than that is selling the novelty rather than the change.

The short version

LLM SEO is not a discipline separate from SEO. It is the same infrastructure judged by a different retrieval mechanism, where passages are the unit and citation replaces position. The platforms differ enough that a single strategy is a simplification, but the underlying work is the same, and it is work you would want done anyway.

Operator note

I am wary of how confident this field sounds. The mechanisms are not documented, the platforms change without notice, and a great deal of what circulates as best practice is one person’s observation generalised into a rule.

What I have kept to is advice that survives being wrong about the mechanism. If assistants turn out to weight something entirely different next year, a site with self-contained sections and specific claims is still a better site. That is the standard I would hold any recommendation here to, including my own.

Frequently asked

What is LLM SEO?

Making your content likely to be retrieved and cited when someone asks an AI assistant a question your content answers. It is not a discipline separate from SEO: most assistants start from a conventional search, so a page that cannot be found will not be cited. What differs is that assistants retrieve passages rather than ranking pages, so the section becomes the unit of optimisation.

Is LLM SEO different from traditional SEO?

Partly. Crawlability, structure, expertise and links still matter and still carry over. Four things genuinely change: the retrieved unit is a section rather than a page, there is no ranking position so you are cited or absent, planning starts from questions rather than keyword volume, and an assistant that learns something wrong about you repeats it consistently to everyone who asks.

Do I need a separate strategy for ChatGPT, Perplexity and AI Overviews?

They differ enough that a single strategy is a simplification. AI Overviews draws on Google’s own index and rewards conventional SEO almost entirely. Perplexity searches first and cites visibly, so ranking plus quotability matters. ChatGPT with browsing sits between them. The common factor is that conventional ranking feeds most of them, which is why the foundation is shared even where the tactics differ. Its effect on clicks specifically, and which queries are worth conceding, is covered in the AI Overviews piece.

Is LLM SEO worth investing in yet?

Assistant usage is growing and measurable referral traffic is small, but nobody can size the influence on buying decisions because no measurement would show it. That argues for doing the work as an editing standard applied to content you already have, rather than as a funded programme competing with channels that have provable return. The changes involved improve the pages regardless of who reads them.