LLMO is the newest three-letter acronym in search, and it describes work that has been quietly underway for years. The label is fresh. The job is not.
LLMO, or Large Language Model Optimisation, is the practice of shaping your content so AI systems like ChatGPT, Gemini, and Google's AI Overviews can find it, understand it, and cite it by name. It overlaps heavily with SEO. It is also different in a few ways that matter, and those differences are exactly where most businesses are currently invisible.
If your organic traffic has been sliding since 2024 with no obvious culprit, LLMO is probably part of the story. The short version: people ask the robot now, the robot answers without sending a click, and if the robot does not mention you, you may as well not exist. Here is what LLMO is and what to actually do about it, minus the part where we pretend it is harder than it is.
What LLMO actually means
LLMO lives in the same family as SEO and AEO, and lately the acronyms have started breeding. The honest distinction is just what you are optimising for:
- SEO gets you ranked on a results page.
- AEO gets you into answer boxes, snippets, and voice replies.
- LLMO (and its near-twin GEO, Generative Engine Optimisation) gets you mentioned inside a generative AI's answer.
If that sounds like hair-splitting, that is because it partly is. LLMO and GEO get used interchangeably by everyone who is not trying to sell you a course on the difference. The work overlaps. What changes is the destination: not a blue link, but a sentence the AI writes with your name in it. Our breakdown of the SEO, AEO and GEO stack untangles the whole alphabet if you want the long version.
The two ways an AI finds you
Here is the bit the acronym debates skip, and it is the most useful thing to understand. An AI can mention you through one of two routes, and they call for slightly different work.
- Training data. The model learned about you when it was built, from the slice of the web it was trained on. This route is slow and sticky: it takes a training refresh to change, but once you are in, you tend to stay. The pages you publish today are auditioning for a model that ships in 18 months.
- Live retrieval. The model looks something up in real time, the way ChatGPT search, Perplexity, and AI Overviews do, then cites what it finds right now. This route is fast and current: fix a page this week and it can show up this week.
Most LLMO advice quietly assumes one route or the other. The useful version does both: write pages good enough to be remembered and structured enough to be retrieved. One plays the long game. The other pays this quarter.
Why your traffic slipped after 2024
If you want a date for when this stopped being optional, mid-2024 is close enough. Three things converged and have compounded since.
- AI Overviews went wide. Google started answering informational queries directly at the top of the page, pushing the actual results below the fold on a growing share of searches. 25.11% of Google searches now trigger an AI Overview, up 57% in a single quarter on Conductor's 2026 numbers.
- The chat box became a search box. More than 800 million people now use ChatGPT every week, and a large chunk of them ask it the questions they used to type into Google.
- Zero-click became normal. On a growing share of queries the answer arrives in full and the user never visits anyone. There is no page-two consolation prize when there is no page two in view.
None of this is reversing. These are not algorithm wobbles you wait out. They are a change in where the answer gets delivered, and the answer increasingly gets delivered without you in the room.
If your traffic fell on informational queries (the "what is", "how to", "why does" searches) while your transactional pages held steady, AI Overviews are the prime suspect. Split your Search Console queries by type and compare the trend lines. The shape of the drop usually tells you who took the traffic.
What actually moves citations
We watched this land for a client last year. A dental group, five practices, perfectly respectable Google rankings. The owner typed "best dentist in [their city]" into ChatGPT one evening, out of idle curiosity rather than strategy. Three competitors came back by name. They did not. They had been checking their Google position for years and had never once thought to check what the AI said, because until recently nobody had. That is usually the evening the LLMO conversation starts.
The good news: the things that get you cited are mostly things a decent writer would do anyway, done on purpose. The research and our own tracking keep landing on the same short list.
Lead with the answer
Models often lift your first paragraph as the quote. Bury the answer under three sentences of throat-clearing and you hand the citation to whoever got to the point faster. Inverted pyramid wins: answer first, context after.
Write sentences that stand on their own
"This cut conversions by 23%" is useless to a machine that cannot see what "this" refers to. "Adding a fourth form field cut conversions by 23%" can be quoted as-is. Self-contained sentences travel. Context-dependent ones get left behind.
Use specific, attributable numbers
"Many studies show" and "a lot of users" paraphrase into nothing. A precise figure with a named source is the kind of thing an AI repeats word for word, because it can stand behind it. Vague is forgettable. Sourced and specific is quotable.
Put the comparison in a table
Tables get extracted disproportionately often. A clean comparison table can earn citations for the page it sits on for years, long after you have forgotten you made it. If you have data begging to be a table, stop describing it in prose.
Worth saying plainly, because plenty of tools promise the opposite: you cannot bulk-generate your way into AI citations. The engines cite sources that show expertise, original data, and a clear structure, and a thousand spun articles have none of those. With 93% of AI search sessions ending without a single click, the handful of sources the model names are the entire prize. Being one of them is earned, not automated.
The half of LLMO most people skip
Everything so far is on-page, and on-page is where most LLMO advice stops. That is a problem, because a large part of whether an AI trusts you happens nowhere near your website.
Language models build a picture of who is credible from how the wider web talks about you. Get mentioned, in the right context, on sources the model already respects, and you start to look like an authority it can safely cite. The levers:
- Brand mentions, not just links. A model counts being talked about, even without a hyperlink attached. Being named alongside your topic, repeatedly, across credible sites is the off-page signal that matters most.
- Digital PR. Getting quoted in industry publications and real news does double duty: good marketing, and exactly the kind of third-party mention the models weight heavily.
- Reddit, forums and user content. Unglamorous, and heavily represented in training data. If your category gets argued about on Reddit and you are never in the thread, the model learns the conversation without you in it.
- Wikipedia and structured references. Models lean on them as fact-checks. You cannot game your way onto Wikipedia, and you should not try, but being genuinely citable enough to belong there is the bar worth aiming at.
This is the part that takes time and cannot be shortcut, which is precisely why it works. Our playbook for getting your brand cited in AI responses goes deeper on the off-page side.
The technical groundwork
None of the above helps if the crawler cannot read the page in the first place. Three technical signals carry most of the weight.
- Let the crawlers in. AI bots like GPTBot have to be allowed in robots.txt. Block them and you also make yourself uncitable, so pick a side knowingly rather than by accident.
- Render on the server. Many AI crawlers do not run JavaScript. If your content only appears after the page hydrates in a browser, those crawlers see a blank room. Server-side rendering or static HTML keeps you visible.
- Mark it up. Article, Organization, FAQPage, Product. Schema spells out what your content means in a format machines read directly. Google's structured data documentation covers the markup.
There is also llms.txt, a newer file that hands AI a curated map of your best pages. Whether it does much yet is genuinely up for debate, and our guide gives it the honest, slightly sceptical treatment it deserves rather than the breathless one.
How to tell if it is working
SEO trained everyone to watch rankings. LLMO does not hand you a tidy position number, which makes "are we winning" a harder question. Three things are worth tracking instead.
- Share of voice. Across a set of prompts a customer might actually ask, how often does the AI name you versus your competitors? That ratio, tracked over time, is the closest thing LLMO has to a ranking.
- Citation tracking. Which specific pages get named, on which platforms, for which questions. This tells you what is working so you can do more of it and less of the rest.
- AI referral traffic. Small but growing, and unusually well-qualified. 87% of AI referral traffic comes from ChatGPT alone, so that is where to look first in your analytics.
You will not get a clean weekly graph out of this yet. The tooling is young and a little held together with tape. But a rough share-of-voice number beats the alternative, which is finding out you are invisible the same way that dental group did.
Where to start this week
You do not need a strategy deck. You need an afternoon and a slightly uncomfortable willingness to see where you stand.
- Baseline yourself. Ask ChatGPT, Gemini, and Perplexity to recommend the best business in your category and city. Write down who they name. If it is not you, that is your starting line, not your verdict.
- Audit your top ten pages. Are the openings answer-first? Are the claims specific and attributed? Do they have schema? Be honest, then be annoyed, then fix them.
- Rewrite three pages around the inverted pyramid and self-contained, quotable sentences.
- Add schema to those pages, and confirm the AI crawlers are not blocked in your robots.txt.
Then repeat next month. LLMO compounds in a way that is almost annoying: the page you fix today becomes the source the model cites long after, and the business that started a year ago is already the answer while everyone else is still arguing about the acronym.
Frequently asked questions
Is LLMO the same as SEO?
No, but they are close relatives. SEO gets you ranked on a search results page; LLMO gets you mentioned inside an AI's answer. They share most of the same foundations (good content, clean technical setup, third-party authority), so strong SEO gives you a head start. The difference is the target: a blue link versus a sentence the model writes with your name in it. You want both.
Is LLMO the same as GEO?
Effectively, yes. LLMO (Large Language Model Optimisation) and GEO (Generative Engine Optimisation) describe the same work: getting cited inside generative AI answers. GEO leans on the word "engine", LLMO on the "model" underneath it. Most practitioners use them interchangeably, and anyone insisting on a hard distinction is usually selling something.
Does LLMO replace traditional SEO?
No, it extends it. The technical foundations, content quality, and authority signals that SEO has always rewarded are most of what LLMO needs too. AI search sits on top of the same web your SEO already lives on. Abandoning SEO to chase LLMO is swapping one half of the job for the other. Do both, because the same work feeds both.
How do I get my content cited by ChatGPT?
Lead with the answer, write self-contained sentences a model can quote without context, back claims with specific attributed numbers, and structure pages with real headings and tables. Make sure GPTBot is not blocked in your robots.txt. Off the page, earn genuine mentions on sites the model already trusts. Then check your share of voice across a set of real customer prompts and keep tightening the pages that are not landing.
How long does LLMO take to work?
It depends which pathway. Live retrieval (ChatGPT search, Perplexity, AI Overviews) can pick up a fixed page within days to weeks. The training-data pathway is slower, because the model only learns new things when it is retrained, which can be many months out. The honest answer: some of it is quick, and the durable part is a compounding play measured in quarters, not days.
Do I need special tools for LLMO?
Not to start. You can baseline yourself by hand in an afternoon: ask the main AI tools to recommend businesses in your category and note who they name. Dedicated platforms that track share of voice and citations across AI engines help once you are doing this at scale, but the first useful audit costs nothing but a slightly bruised ego.
LLMO is not a new discipline so much as the next room of the same house. Write like a credible source, get talked about like one, and make the whole thing readable by a machine, and you start showing up where the answers now happen. If you would rather not spend your quarter rewriting openings and chasing brand mentions, that is what we are for. Our AI SEO service builds LLMO into every plan from $300 a month, no contracts, with pricing on the website and the same number for everyone. Want to know where you stand first? Grab a free SEO and AI audit and we will ask the robots about you, then tell you honestly what they said. Fair warning: it is occasionally a humbling read.
