LLM SEO: what the term means, and what is actually different
The name is unfortunate and the category is real. A working definition, where the vocabulary came from, how the major assistants differ, what the evidence says about the popular claims, and an honest way to size the work.
What is LLM SEO?
LLM SEO is the practice of making your content and brand likely to be retrieved, quoted and cited when a large language model, such as the ones behind ChatGPT, Claude, Gemini, Perplexity or Copilot, answers a question your content can answer. It builds on classic SEO and differs mainly in what is retrieved and how success is counted.
The useful definition is narrow. Everything sold under the name is either conventional SEO, ordinary content quality, a genuinely new requirement, or invention. Sorting a recommendation into one of those four is most of the skill.
What makes LLM SEO a distinct concern rather than a rebrand is the mechanism. A search engine ranks documents and a person chooses among them. An assistant retrieves passages and a model writes one answer, usually citing a handful of sources. The middle step is gone: nobody chooses your result from a list, because the user never sees the list. That single difference produces every real change in the work, and much of the discussion gets it wrong by treating an assistant as a search engine with a chat box.
The mechanics of that retrieval, from query fan-out to the passages that get selected, are set out in the AI search optimization guide to what gets retrieved. This page is about the term, the evidence, and what the work is worth.
LLM SEO, GEO, AEO and LLMO: one field, several names
LLM SEO, GEO (generative engine optimisation), AEO (answer engine optimisation), LLMO (large language model optimisation) and AI SEO all describe roughly the same work: earning a place in AI-generated answers. The names differ in emphasis and origin, not in what you would actually do on Monday.
| Term | Where it comes from | What it emphasises |
|---|---|---|
| GEO | A 2023 research paper that coined “Generative Engine Optimization” | Visibility inside generated answers |
| AEO | Practitioner usage | Being the direct answer to a question |
| LLM SEO / LLMO | Practitioner usage | The model as the audience, including what it learned in training |
| AI SEO / AI search optimization | Practitioner and vendor usage | Any AI-driven search surface, Google’s included |
The one term with a precise origin is GEO. It comes from GEO: Generative Engine Optimization, a paper by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande, presented at KDD 2024, the ACM conference on knowledge discovery and data mining. The others grew out of practice and vendor marketing, which is why their definitions drift.
Google has taken a side on the naming question. Its guide to optimizing for generative AI features, which addresses the AEO and GEO labels directly, says that optimising for generative AI search “is optimizing for the search experience, and thus still SEO”. That is Google speaking about its own surfaces, not about ChatGPT or Claude, but it matches what the evidence below shows for the other assistants too.
For planning purposes the names do not matter. What matters is whether a recommendation acts on the passage, on ranking, on third-party mentions or on crawler access, because those are the four places the work happens.
- Quotations, statistics and cited sources did best in the GEO paper
- Costs editing time only, and improves the page for people too
- Helps only pages that are already retrieved
- The paper’s percentages come from a lab setup
- Top-ranked sub-query results are cited far more often (AirOps)
- Same work as the SEO programme you already run
- Slow and competitive
- Google does not publish its fan-out queries
- The only lever on how review sites and forums describe you
- Reaches answers that come from training data, not search
- Outreach-heavy and slow
- Hard to attribute to any single result
- One afternoon to check
- A blocked search crawler rules out citation entirely
- A gate, not a gain: allowing a crawler earns nothing by itself
What the GEO paper actually tested
The GEO paper tested whether rewriting source content changes how visible it is inside answers from generative engines, using a benchmark called GEO-bench of 10,000 queries across several domains. Its headline finding is that the methods “can boost visibility by up to 40% in generative engine responses”.
The methods that worked best in the paper’s experiments were adding quotations, adding statistics and citing sources. Making text more fluent and easier to understand also helped, by 15% to 30% in the paper’s tests. Keyword stuffing, the classic SEO tactic, offered little or no improvement. The paper also reports gains of up to 37% on one measure when tested on Perplexity, and notes that the most effective method varied by domain.
| Method | Result in the paper | Practical version |
|---|---|---|
| Quotation addition | Among the strongest | Quote named experts or primary documents |
| Statistics addition | Among the strongest | Give specific numbers with unit, population and date |
| Cite sources | Among the strongest | Link claims to primary sources |
| Fluency and readability | Helpful | Plain sentences, one idea each |
| Keyword stuffing | Little or no improvement | Do not do it |
Two limits are worth holding onto. First, the main experiments ran on an engine the authors built themselves, which fetched the top five Google results for each query and wrote the answer with GPT-3.5-turbo; the Perplexity check used a 200-query subset with the source text uploaded as files. The percentages describe that setup, not a commercial assistant in the wild. Second, each method was applied to one source while the other sources for the same query were left unchanged, which is a fair model of competing for a citation but assumes the page has already been retrieved. The direction is the useful part: specific, sourced, quotable statements get used, and repeated keywords do not.
How an LLM knows about your brand: training and retrieval
A large language model knows about your brand in two ways: from its training data, which is a snapshot taken before the model was released, and from retrieval, a live web search run while it answers. Only retrieval responds to changes you make this quarter; training changes when a new model is trained.
| Training (parametric knowledge) | Retrieval (live search) | |
|---|---|---|
| When it is formed | Before the model ships | At the moment of the question |
| Which crawlers feed it | GPTBot, ClaudeBot; Google-Extended controls Google’s use | OAI-SearchBot, PerplexityBot, Claude-SearchBot, Googlebot |
| How fast your changes show | Only in a later model | After recrawl and re-ranking |
| What moves it | What is widely and consistently published about you | Ranking for sub-queries and quotable passages |
The crawler names in that table come from each vendor’s documentation. OpenAI’s crawler overview says GPTBot collects content “that may be used in training our generative AI foundation models”, while OAI-SearchBot is “used to surface websites in search results in ChatGPT’s search features”. Anthropic’s help article separates ClaudeBot, which collects content that could contribute to training, from Claude-SearchBot, which improves search result quality. Google’s AI features documentation says Google-Extended governs training and grounding in other Google AI systems, separately from Search.
Should you block training crawlers?
The answer depends on what the content is for. Either way, never block the search crawlers by accident: OpenAI states that sites opted out of OAI-SearchBot “will not be shown in ChatGPT search answers”.
Yes
Yes
No
No, we sell software or services
Yes
No
The training path explains a failure that surprises teams: an assistant repeating an outdated fact about a product months after the website changed. Nothing on the site can fix that answer directly. The indirect fix is making the correct fact consistent wherever the product is described, which is the brand-level work in how to improve brand visibility in AI search.
LLM SEO vs traditional SEO: what changes and what does not
LLM SEO keeps the foundation of traditional SEO, which is crawlable, indexable pages that rank, and changes four things on top: the unit is the passage, there is no partial credit for position, planning starts from questions, and errors about your brand spread differently.
| Dimension | Traditional SEO | LLM SEO |
|---|---|---|
| Unit retrieved | Page | Passage or section |
| Outcome | Ranking position and click | Cited, mentioned, or absent |
| Partial credit | Position 4 still earns some clicks | Little: in the answer or not |
| Planning input | Keywords with volume | Questions, and the sub-queries they fan out into |
| Off-site signal that matters most | Links | Brand mentions on the pages models read |
| Error mode | A bad snippet among ten results | A wrong fact repeated to everyone who asks |
| Measurement | Rank tracking, Search Console | Question panels, platform citation reports, referrals |
The evidence that the foundation still matters is consistent. Ahrefs analysed 1.4 million ChatGPT prompts and found results from the general search index were cited 88.46% of the time they were retrieved. Seer Interactive found Google page-one rankings correlated with brand mentions in AI answers at about 0.65. AirOps found that the result ranked first for a ChatGPT sub-query was cited 58.4% of the time, against 14.2% for the result ranked tenth. A page that cannot be found by search will not be cited by an assistant that starts by searching, which is most of them.
The most underrated row in the table is the error mode. Conventional search shows several results and lets a user compare. An assistant gives one confident answer. If that answer contains a wrong fact about your pricing or your capabilities, every person asking receives it, and there is no second result beside it to prompt doubt.
How ChatGPT, Perplexity, Google, Copilot and Claude differ
The major assistants differ in which index they search, which crawler feeds it, and what reporting they give site owners, so a single LLM SEO strategy is a simplification. The shared foundation is that all of them can search the web, and all of them reward pages that rank and read cleanly in extract.
| Assistant | Search crawler | Index and behaviour | Reporting for site owners |
|---|---|---|---|
| ChatGPT | OAI-SearchBot; ChatGPT-User for user requests | Surfaces sites in ChatGPT search; can also answer from training | Referrals in analytics |
| Perplexity | PerplexityBot; Perplexity-User for user requests | Surfaces and links websites in its answers | Referrals in analytics |
| Google AI Overviews and AI Mode | Googlebot | Google’s index, with query fan-out across subtopics | Clicks in Search Console’s Web search type; impressions in a beta Generative AI report |
| Microsoft Copilot | Bingbot, with site controls in Bing Webmaster Tools | Grounds answers in retrieved web content | AI Performance report in Bing Webmaster Tools |
| Claude | Claude-SearchBot; Claude-User for user requests | Indexes for search quality; fetches pages for user queries | Referrals in analytics |
Two rows stand out for practical reasons. Google says in its documentation that there are “no additional requirements to appear in AI Overviews or AI Mode” beyond being indexed and eligible for a snippet, so for Google, LLM SEO is conventional SEO plus passage quality. Microsoft offers a dedicated citation report: Bing Webmaster Tools’ AI Performance report, launched in February 2026, shows total citations, cited pages and grounding queries across Copilot and Bing’s AI summaries.
Perplexity’s documentation is unusually clear about its fetchers: PerplexityBot surfaces and links sites in search results, while Perplexity-User fetches pages for a user’s question and “generally ignores robots.txt rules”. The platform-specific detail for the two most asked-about surfaces is in separate guides: AI Overviews and what to do about the clicks, and how ChatGPT picks its sources.
The LLM SEO claims worth ignoring
The LLM SEO claims most worth ignoring are that schema markup gets you into AI answers, that you need an llms.txt file, that traditional SEO is dead, and that anyone has the ranking factor list for a specific model. Each now has evidence against it, or none for it.
| Claim | Evidence | Verdict |
|---|---|---|
| Schema markup is how you get into AI answers | 1,885 pages added JSON-LD; no meaningful citation change on ChatGPT or AI Mode after 30 days (Ahrefs) | Keep it for rich results; it is not the lever |
| You need an llms.txt file | 97% of published llms.txt files got no requests in a month across 137,210 domains (Ahrefs); Google says no AI text files are needed | Optional and unproven |
| Traditional SEO is dead | Search-index results cited 88.46% of the time retrieved (Ahrefs); page-one rank correlates about 0.65 with mentions (Seer) | False; it is the foundation |
| Here is the ranking factor list for ChatGPT | No vendor publishes weightings; Google says no special optimisation is needed for its AI features | Treat as marketing |
The detail behind the first two rows, the Ahrefs schema test with matched control pages and the Ahrefs llms.txt study, is set out in the AI search optimization guide linked above, so it is not repeated here. One piece of background is worth knowing: llms.txt was proposed by Jeremy Howard in 2024 as a file that guides AI systems to a site’s content, and it has stayed a proposal. Google’s AI optimization guide says such files are not needed, and that Google Search itself does not use them.
How to evaluate an LLM SEO vendor or agency
The same test applies to anyone selling LLM SEO, GEO or AEO services. A credible provider can answer these questions plainly; a weak one answers with a proprietary score.
- Which prompts will you track, and who chooses them? They should be your buyers’ questions, without your brand name, agreed before work starts.
- How many runs per prompt, in which assistants, in what kind of session? One logged-in run per month is not measurement.
- What will you change on the site, and what off it? Expect crawler access, passage edits, brand facts and third-party outreach. Be wary of a plan that is mostly schema and llms.txt.
- What would count as failure at 90 days? A provider who cannot name one has not defined success either.
- Do you claim to place content in a model’s training data? Nobody outside the model vendors can do that on demand, so a yes is a reason to walk away.
The common thread is false precision. The honest state of knowledge is that the direction is clear and the specifics are not. A useful test for any recommendation: does it still make the page better if the mechanism turns out to work differently? If yes, follow it. If it only pays off under one theory of how a model works, it is a bet dressed as a best practice.
Is LLM SEO worth it? How to size the opportunity
LLM SEO is worth doing as an editing standard on content you already have, because the cost is low and the changes improve the pages anyway, but on current data it rarely justifies moving budget away from channels with measurable return. Assistant usage is large; assistant referral traffic is still small.
On usage: OpenAI said in February 2026 that ChatGPT had reached 900 million weekly active users. OpenAI’s own analysis of 1.5 million conversations found that practical guidance, seeking information and writing together made up about three-quarters of conversations. On traffic: Ahrefs estimated that ChatGPT gets about 12% of Google’s search volume for traditional search-type queries, yet across 76,000 websites Google sent 190 times more traffic, making up nearly 40% of site visits against 0.21% for ChatGPT.
A worked sizing example
Take a hypothetical B2B site that receives 40,000 sessions a month from Google organic search. If its traffic mix matched the Ahrefs sample, where Google sent 190 times more traffic to websites than ChatGPT, ChatGPT would send about 40,000 divided by 190, or roughly 210 sessions a month. Even a large improvement in citations moves that number by tens or low hundreds of sessions.
The calculator puts a ceiling on what referral traffic alone can justify. It uses assistant sessions = Google sessions / ratio, then extra sessions a year = assistant sessions * 12 * rise / 100, extra conversions = extra sessions * conversion rate / 100 and value = extra conversions * value per conversion. The default ratio of 190 is the Ahrefs figure for Google against ChatGPT across 76,000 sites (Google’s share of all their visits, 39.98%, divided by ChatGPT’s 0.21%), not a per-site average; if GA4 already shows your AI Assistant channel, divide your Google organic sessions by those sessions and use your own ratio instead.
Enter your own traffic and economics to see what assistant referrals could be worth.
That is why referral traffic is the wrong way to size LLM SEO. The value sits in the answers that are read without a click: whether your brand is named when a buyer asks for a shortlist, and whether what is said about you is correct. Nobody can yet measure how many purchases those answers influence, and anyone giving you a number for it is guessing. The defensible position is the asymmetry: self-contained sections, named entities and accurate brand facts cost editing time and improve the pages whoever reads them, so the downside of doing the work is close to nothing. Attribution for channels like this is discussed in measuring organic when attribution breaks.
Who LLM SEO matters for, and who it does not
LLM SEO matters most for companies selling a considered purchase, especially in a new or unfamiliar category, and least for local services, impulse purchases and brands whose buyers already search for them by name. The urgency is uneven, and treating it as universal is how it gets over-funded.
| Situation | How much it matters | Why |
|---|---|---|
| Technical or considered B2B purchase | A lot | Buyers research at length, and a shortlist question is a natural assistant query |
| New or unfamiliar category | A lot | The assistant shapes which vendors are considered at all |
| Established brand, strong direct demand | Less, for now | Buyers search your name; the risk is slower and sits with the next cohort |
| Local or physical services | Less, so far | Local results and listings remain the main route; check your own panel |
| Impulse or low-consideration purchase | Little | Few people consult an assistant before a small routine buy |
The second row is where acting early pays most. In a category buyers do not yet have a mental list for, the assistant is effectively assembling the shortlist, and absence 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, because its buyers already know who they are looking for.
A practical decision rule follows from the table. If more than half of your new pipeline already comes from people searching your brand name, spend on accuracy first: the brand fact sheet and panel questions about your own pricing and capabilities. If most pipeline starts from category or problem searches, spend on the source-gap work and original data that get you named in shortlist answers, because those are the questions assistants increasingly field.
Common LLM SEO mistakes, and how to spot them
The most common LLM SEO mistakes are treating it as a separate channel, counting only citations of your own URLs, blocking search crawlers by accident, testing in a personal account, and rewriting the whole site before proving the approach on a few pages. Each has a quick check.
| Mistake | Symptom | Check |
|---|---|---|
| Separate AI channel with its own owner | A roadmap and a report, but few changed pages | Count pages edited to the passage standard last quarter |
| Counting only your own citations | Reporting says little has changed while competitors are named in answers | Add mentions and recommendations to the panel |
| Search crawlers blocked | Zero OAI-SearchBot or PerplexityBot requests in server logs | Filter logs by user agent and status code |
| Testing in a logged-in account | Results look better than prospects report | Rerun the same questions in a temporary session |
| Leading questions | Your brand appears in every answer | Remove the brand name from every panel question |
| Site-wide rewrite first | Months of editing, no measurable change | Pilot on ten ranking pages and compare with ten untouched ones |
| Trusting an opaque visibility score | A number moves, but nobody can say why | Ask the vendor which prompts, how many runs, and which models |
The pilot in the sixth row deserves a sentence of method. Choose twenty pages with similar traffic and question-shaped queries, edit ten to the passage standard and leave ten alone, then run the panel questions those pages target for two or three months. If the edited group gains citations or mentions and the control group does not, you have evidence specific to your site that no published study can give you. If neither moves, you have saved yourself a site-wide rewrite.
LLM SEO tools: what each one can and cannot tell you
LLM SEO tools fall into five groups: your own server logs, search engine webmaster tools, AI visibility trackers, entity and passage checkers, and a plain spreadsheet for a manual panel. None of them measures influence on buying decisions, and each answers a narrower question than its marketing suggests.
| Tool | Question it answers | What it cannot tell you |
|---|---|---|
| Server logs or a log analyser | Are AI search crawlers reaching my pages, and with what status codes? | Whether anything they fetched was cited |
| Bing Webmaster Tools AI Performance | How often are my pages cited in Copilot and Bing’s AI summaries, and for which grounding queries? | Anything about ChatGPT, Perplexity or Claude |
| Google Search Console | How do my pages perform in Search, and which appear in AI features (beta Generative AI report, impressions only)? | Clicks or queries for AI features separately from classic results |
| AI visibility trackers | How often is my brand named across a large prompt set? | Results for prompts outside their set; their sampling method unless disclosed |
| Entity and passage checkers | Does this page name what it discusses, and do its sections stand alone? | Whether the page ranks for the sub-queries |
| Spreadsheet panel | For my own buyer questions, am I cited, mentioned or absent? | Anything at scale; it is slow but fully understood |
A sensible order for a small team is free first: logs, Bing Webmaster Tools and a spreadsheet panel cost nothing and answer the questions that matter in the first quarter. A paid tracker earns its place once the panel has shown that assistant visibility is moving and you need more prompts, more markets or competitor history than a spreadsheet can hold; the questions to put to a vendor are in the tracking guide linked below.
How to add LLM SEO to an existing SEO programme
LLM SEO fits into an existing SEO programme as four additions: a crawler policy, an editing standard for passages, a monthly question panel, and a plan for original data and third-party mentions. None of them needs its own team, and all of them improve the conventional programme too.
An LLM SEO checklist
Who owns what
- The SEO lead owns the crawler policy, the panel and the reporting, because they share inputs with rank tracking and technical SEO.
- The content lead owns the editing standard, because it changes how every page is written.
- PR or partnerships own third-party mentions, because the work is outreach and relationships.
- Product marketing owns the brand fact sheet, because it owns positioning and pricing facts.
Week 1, month 1, quarter 1
In week 1, set the crawler policy and run the first panel to record a baseline. In month 1, retrofit the editing standard to the twenty pages that bring the most organic impressions for question-shaped queries, and check them with the Entity Extractor to see whether they name what they discuss. By the end of quarter 1, publish one piece of original data, rerun the panel twice, and decide on evidence whether the work deserves more time. How to run and read the panel itself is covered in how to track brand mentions in AI search.
The organisational point is the one that decides whether any of this happens. A separate AI visibility owner produces a separate roadmap and a quarterly document. The people who already own content quality and SEO, adding four items to their existing work, produce changed pages. Where a team wants the whole of it run as one engagement, that is the scope of the AI search visibility service; where the underlying SEO foundation is the weak point, SEO systems work comes first.
LLM SEO is the work of making content likely to be retrieved and cited when a large language model answers a question, and it is mostly SEO judged by a different mechanism. Models know about you through training or through live search, and only the second responds on a useful timescale. The evidence so far supports ranking, self-contained passages and third-party mentions, and does not support schema or llms.txt as levers. Fold it into the SEO programme as an editing standard and a monthly question panel rather than funding a separate initiative.
I am wary of how confident this field sounds. The mechanisms are not documented, the platforms change without notice, and much of what circulates as best practice is one observation generalised into a rule.
The standard I hold any recommendation to, including my own, is whether it survives being wrong about the mechanism. If assistants turn out to weight something different next year, a site with self-contained sections, specific claims and consistent brand facts is still a better site.
Frequently asked
What is LLM SEO?
Is LLM SEO the same as GEO or AEO?
How is LLM SEO different from traditional SEO?
Does LLM SEO replace SEO?
Can I get my brand into an LLM’s training data?
Do I need llms.txt or schema for LLM SEO?
Is LLM SEO worth investing in?
How do I measure LLM SEO?
I take on a small number of engagements at a time. If you are past product-market fit and want to talk through what this would look like for your situation, the calendar is open.
Book a call