People keep asking me if they need to forget everything they know about SEO. My answer is always the same: no, but you do need to understand what just got added to the game.
The naming is the first thing to sort out. LLM SEO, GEO, AEO: they’re all the same discipline with different badges. Everyone is trying to coin the definitive term. At its core, it’s search engine optimisation applied to AI search platforms. Or, as I prefer to think about it: search everywhere optimisation.
What’s changed is not the goal. The goal is still to help people find useful information. What’s changed is where they look.
What LLM SEO actually is
Traditional search engines give you a list of links. You click through, read the sources, piece together an answer yourself.
AI search engines synthesise. They collect information from across the web, assemble it into a direct answer, and surface the most useful parts, often without requiring a click.
That mechanism is what changes things. The engine is not sending traffic to pages the way it used to. It’s reading pages, extracting relevant passages, and presenting those passages directly in the conversation.
LLM SEO is the practice of making your content the thing that gets read, extracted, and cited.
It’s an extension of what we already do, not a replacement. The AI engines still rely on search indices (Bing, Google, Brave) to validate and retrieve sources. The foundational signals that have always mattered (authority, relevance, trustworthiness, well-structured content) still apply. The difference is in how AI platforms consume and surface that content once they’ve found it.
I explain it to clients like this: Google was the librarian who pointed you to the right shelf. ChatGPT, Perplexity, and Google’s AI Mode are the librarian who reads the book for you and summarises the chapter you needed. Your job as a content creator hasn’t fundamentally changed: write something worth reading. But the way that content gets discovered and surfaced is different enough to warrant its own attention.
If you want to understand how LLM SEO relates to generative engine optimisation as a broader discipline, I’ve written a deeper comparison of GEO vs SEO that covers where the two frameworks overlap and where they diverge.
Why LLM search matters now (and what the numbers actually say)
The honest version first: AI search is not replacing Google. Not this year, probably not next year.
But the direction of travel is clear.
Between 64% and 68% of Google searches in 2026 end without a click. Users get what they need from the results page itself. Add AI Overviews and Google’s AI Mode to that picture. AI Overview queries have an 83% zero-click rate; AI Mode sits at 93%. That’s why organic click-through has become harder to predict and protect.
AI search platforms are filling some of the gap that creates. ChatGPT is present in four out of five LLM search sessions. Referral traffic from AI sources grew roughly 80% year-over-year through 2025. The share is still small relative to Google, but it’s growing fast and from a standing start.
What’s more interesting is the quality of that traffic. Sessions from AI search platforms convert at a higher rate. One study put revenue per session from ChatGPT traffic at 31% higher than non-branded organic. My experience with clients aligns with that directionally. People arriving from AI search tend to be further along in their thinking. They’ve already had a research conversation with an AI assistant. They know what they’re looking for.
One thing on citations specifically: if your brand appears in an AI answer, there’s a halo effect in traditional search. Cited brands earn roughly 120% more organic clicks per impression than uncited ones. But citations themselves are not a clean primary metric. They shift from query to query, person to person. Treat citation data the way you’d treat impressions in Google Search Console: a useful indicator of visibility, not the measure of success.
The reason this matters for strategy is not that AI search will overtake Google. It’s that more people are using AI tools as their first step in research. If you’re absent from those conversations, you’re missing part of the picture.
How LLMs actually find and use your content
Most LLM SEO advice skips this part. The mechanism is what separates tactics from strategy.
When someone asks an AI search engine a question, the engine doesn’t read your page the way a human does. It runs a retrieval process, pulling relevant passages from across the web, scoring each passage for relevance, and assembling an answer from the most useful chunks. This is called RAG: Retrieval-Augmented Generation.
What it means practically: the engine is not evaluating your page as a whole. It’s scoring individual passages. A 3,000-word article might yield three or four chunks the engine finds useful, each pulled independently.
Research into citation patterns reflects this. Content from the first 30% of an article accounts for 44.2% of LLM citations. The opening sections of your content matter disproportionately. Not because LLMs ignore the rest, but because well-structured early content tends to contain the direct, declarative answers the retrieval system is optimised to find. If your article spends 600 words warming up before it says anything concrete, the retrieval system has probably moved on.
The second mechanism worth understanding is query fan-out.
When someone asks an AI engine a question, the engine doesn’t run one search. It runs several, breaking the original question into sub-queries and retrieving separate answers for each before synthesising a response. A question like “how does LLM SEO work” might generate sub-queries around what LLMs are, how they retrieve information, what makes content citable, and how this differs from traditional search.
If you want to be cited across those sub-queries, you need to answer not just the question someone asked, but the questions the AI platform generated in the process of answering it. This changes how you should think about content structure: from “covering a topic” to “covering a topic and all the questions that naturally surround it.”
What actually moves the needle
The one thing I keep coming back to when advising on LLM SEO: it’s more personal than traditional search.
Google’s algorithm is largely indifferent to who’s asking. Two people searching the same keyword get roughly the same results. AI search is different. These platforms build user context: previous conversations, stated preferences, implicit signals. The answers they generate are increasingly shaped by who’s asking and what conversation preceded the question.
That personalisation matters for strategy. You cannot just optimise for a keyword. You need to understand who you’re trying to reach and what that specific person is trying to figure out. ICP and persona work, which many SEO-focused businesses skip or treat as a one-day workshop exercise, becomes more load-bearing in an AI search world. Start there, before any tactics.
Content completeness over keyword density. AI platforms retrieve passages that directly answer questions. Content that hedges, qualifies, and buries its answer (written to signal expertise rather than deliver it) performs worse than content that states things plainly. Write for the person. The answer should be in the first paragraph, not the conclusion.
Query fan-out FAQ optimisation. Most sites have FAQs that are marketing copy with a question mark appended. Replace those with real follow-up questions: the things someone would genuinely ask next, with substantive answers and links to deeper pages for each one. This is the single highest-leverage structural change you can make this quarter. The AI retrieval systems are optimised to find exactly this pattern.
Entity-based link building. When we shifted link-building strategy for clients, we moved away from generic anchor text toward brand + keyword combinations. “Bishops Move Edinburgh removals” rather than “learn more.” The goal is to build entity relationships: clear signals to retrieval systems that this brand is associated with this service and this location. That brand-plus-context combination gets picked up across the citation layer more reliably than authority signals alone.
For one client, the combination of expanding content to cover the full query fan-out and shifting to entity-focused link building produced a steady rise in AI citations alongside an increase in referral traffic from AI sources. It took months, not weeks. But it was methodical and it was repeatable.
Traditional SEO as the foundation. None of the above replaces it. The AI platforms validate sources against search indices. A site with poor authority, thin content, or technical problems will not be retrieved and cited regardless of how well the content is structured for AI consumption. Everything above is additive: it extends good SEO, it doesn’t substitute for it. For a more detailed comparison, the GEO vs SEO breakdown covers where the strategies overlap and where they require separate attention.
How to measure LLM SEO (and what not to obsess over)
Traffic and conversions first. Always.
These are the metrics that reflect something real. Traffic tells you the content is being found. Conversions tell you the people finding it are the right people. Everything else is secondary.
For AI search specifically: check your referral traffic data. Google Analytics and most analytics platforms now surface traffic from ChatGPT, Perplexity, Copilot, and other AI sources as distinct referrers. That’s your clearest signal of AI-driven visits: actual sessions, not inferred visibility.
Citations are harder to track, and worth being honest about why. There’s no native analytics inside most AI platforms. Third-party citation tracking tools run query samples, not comprehensive monitoring. The results vary between tools, and citations themselves vary. The same query produces different citations for different users, at different times, in different conversation contexts.
I treat citation data the way I treat impressions in Search Console: useful directional information, not a number to put in a primary KPI report. If citations are rising, something is working. If they’re falling, something has changed. But I wouldn’t optimise specifically for citations at the expense of the actual goal: people finding the content useful and taking action as a result.
If you want to track brand visibility in AI outputs more systematically, there’s a practical guide to monitoring ChatGPT citations that covers what tools exist and what you can reasonably expect from each.
You’ve spent years on Google rankings. Should you be worried?
No. But pay attention.
The AI engines do not circumvent strong SEO. They validate against it. Google, Bing, and Brave are still the indices these platforms use to verify sources worth citing. If you rank well in traditional search, you are more likely to be retrieved by AI search, not less.
What I’ve seen across clients is that the sites doing disciplined SEO work (clear content structures, real topic depth, legitimate authority signals) were already in a reasonable position when AI search started mattering. They needed to add some levers, not rebuild from scratch.
The unique levers for AI search are real: query fan-out content structuring, entity link building, content completeness, ICP-aligned targeting. These are not hard to layer onto a site that’s already well-optimised. They’re considerably harder to retrofit onto a site that skipped the fundamentals.
The one thing I’d flag: SEO is more important now than it was two years ago. Not less. There’s a narrative in the market that AI tools reduce the need for good SEO, that publishing AI-generated content at scale or skipping link building is now acceptable because AI changes the game. It doesn’t. The AI engines will get better at identifying thin, low-credibility content the way Google did over years of fighting spam. The sites that stay focused on genuine quality will be the ones that benefit from both search environments as they mature.
If you’re doing good SEO, keep doing it. Add the AI-specific layers on top. Measure what changes. Iterate.
Frequently asked questions
Is LLM SEO the same as GEO?
Yes. GEO (generative engine optimisation), LLM SEO, and AEO (answer engine optimisation) describe the same practice. Different practitioners and tools use different terms, and more will emerge. At the core, it’s search engine optimisation applied to AI platforms, or search everywhere optimisation if you want a frame that captures both surfaces.
Does ranking on Google help with LLM citations?
Directly, yes. Most AI search platforms use search indices (Google, Bing, Brave) to validate sources before citing them. Strong organic rankings improve the likelihood that an AI platform will retrieve and surface your content. Traditional SEO and LLM SEO are not competing strategies; one supports the other.
How do I know if LLMs are citing my content?
Start with your analytics: check referral traffic from ChatGPT.com, Perplexity.ai, Copilot, and similar sources. For citation monitoring beyond referral data, third-party tools like Profound, Otterly, and various share-of-voice trackers run query sampling. No tool gives you a complete picture. Treat the data as directional rather than definitive.
What’s the fastest way to improve my chances of being cited?
Rewrite your FAQ sections using a query fan-out approach. Take your existing FAQs and replace them with the real follow-up questions a reader would genuinely ask next, with substantive answers and links to deeper pages. This is the structural pattern AI retrieval systems are built to find. If you have FAQs that are currently marketing copy dressed as questions, changing those is the highest-leverage move you can make this quarter.
Do backlinks still matter for LLM SEO?
Yes, and the type of anchor text matters more than it used to. Links using brand + keyword combinations, building entity relationships between a brand and its core topics, are more useful in an LLM SEO context than generic anchor text. Authority signals still shape how AI platforms assess source credibility.
Is LLM SEO relevant outside the US?
The platforms are globally available, but adoption varies by market. AI search usage is highest in the US, UK, and parts of Asia-Pacific. The underlying optimisation principles (content completeness, query fan-out structuring, entity signals) apply regardless of market. If your audience is searching for information, some portion of them are using AI tools to do it.
Can AI-generated content perform in LLM search?
It can appear in results, but quality signals increasingly matter. AI platforms are developing the ability to distinguish thin, templated content from genuine expert content, the same evolution Google went through with Panda and the helpful content updates. Content written by people with real experience and a specific point of view will outperform generated content at scale as these systems mature.
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