Five new acronyms turned up in SEO in about eighteen months. GEO, AEO, LLMO, AIO, AI SEO. Agencies now sell them as separate services with separate retainers.
Most of them describe the same work.
I have been running SEO for client sites through the whole shift, from the first AI Overviews rollout to the current state where a chunk of informational traffic never reaches a website at all. What follows is the honest version: what each term means, which ones have a real definition behind them, and where the actual work differs. If you only read one section, read the last one.
What is AI search optimization?
AI search optimization is the practice of making a website’s content retrievable, parseable and quotable by AI answer systems such as Google AI Overviews, ChatGPT, Perplexity and Gemini. It covers crawler access, passage structure, entity clarity and off-site brand mentions, measured by citation share rather than by ranking position.
The mechanism is worth understanding before the vocabulary. An AI answer system does three things in sequence. It retrieves candidate documents, it selects passages from those documents, and it synthesises an answer with citations attached to some of those passages.
Traditional SEO optimises the first step. It gets your page into the candidate set. That still matters, and it matters more than most GEO vendors admit, because a page that cannot be retrieved cannot be cited. The mechanics behind that first step are covered in full in the SEO guide.
The new work sits in the second step. Passage selection rewards content that answers a question completely inside a short, self-contained block, with a clear subject and a verifiable claim. A page can rank third in blue links and never get quoted once, because every useful sentence in it depends on the three paragraphs above it.
That gap between ranking and being cited is the whole reason these new terms exist.
What is GEO (Generative Engine Optimization)?
GEO, or Generative Engine Optimization, is the practice of structuring and writing content so generative AI systems select and cite it inside their answers. The term comes from a 2024 KDD research paper and now serves as the industry’s default label for optimizing visibility in AI-generated responses rather than in ranked links.
GEO is the only one of the new acronyms with an academic paper behind it. Aggarwal and colleagues published “GEO: Generative Engine Optimization” (arXiv 2311.09735) and presented it at KDD 2024. They built a benchmark of roughly 10,000 queries across nine domains and tested which content edits changed visibility in generated answers.
Their finding: content edits produced visibility lifts of up to about 40% in their test setup. The techniques that worked best were adding citations to authoritative sources, adding quotations, and adding statistics. Keyword stuffing did close to nothing.
Treat that number as one controlled study on a benchmark, not as a client guarantee. Confidence: medium. The direction of the finding has held up in my own work, but the size of the lift depends entirely on the query and the competition. I go deeper into what gets a passage picked in how AI Overviews choose which sites to cite.
What to do about it: take your five highest-value informational pages and add a sourced statistic and a named citation to each major section. That single edit does more for GEO than anything else available to most sites, and it takes an afternoon. For the full study behind this, see the GEO deep-dive.
What is AEO (Answer Engine Optimization)?
AEO, or Answer Engine Optimization, is the practice of formatting content so search systems can lift a direct answer from it. It predates GEO and originally described featured snippet and voice search optimization. Today the two terms overlap heavily, and most agencies use them interchangeably.
AEO is older than people think. The work started around featured snippets in 2014 and expanded into voice assistants. The format rules from that era still apply: a 40 to 60 word answer immediately under the question heading, a definition that stands alone, tables for comparisons, numbered lists for processes. The exact format is broken down in writing definition blocks that AI answers quote.
The reason AEO survived into the AI era is that its output format happens to be exactly what passage retrieval wants. A self-contained answer block was good for snippets and is good for citations. Same artifact, different consumer.
Where AEO and GEO genuinely differ is intent. AEO targets a single extracted answer. GEO targets inclusion in a synthesised, multi-source answer where you are one of four cited domains. In the first case you win or you do not. In the second you are competing for share.
What to do about it: put a bolded question and a 40 to 60 word answer at the top of every H2 on your commercial pages. Then search that exact question and read what Google currently pulls. If your answer is longer than what it pulls, cut it. For a full side-by-side, see AEO vs GEO: the real difference.
What is LLMO (Large Language Model Optimization)?
LLMO, or Large Language Model Optimization, is the practice of shaping how a brand is represented inside language models themselves, through training data, retrieval sources and off-site mentions. It is less about page formatting and more about whether the model knows what your company is and describes it accurately.
LLMO is the term I use most carefully, because the name promises more control than anyone has. Nobody outside the labs optimizes a model’s weights.
What you can influence is the material the model reads. That means the places a model retrieves from when it answers a question about your category: Reddit threads, review sites, Wikipedia, industry directories, comparison articles, and news coverage. A brand that appears consistently across those sources with the same description gets described accurately. A brand that appears in four places with four different positioning statements gets described vaguely or not at all. I cover how to work that list in getting your brand mentioned by ChatGPT and Perplexity.
The practical test takes two minutes. Ask ChatGPT, Gemini and Perplexity “what does [your company] do” and “who are the best [your category] providers in [your city]”. Write down what comes back. That is your baseline, and it is usually uncomfortable.
Confidence in LLMO as a distinct discipline: medium. The work is real. The label is contested, and Google has never used it.
What to do about it: audit the top ten sources that AI systems cite for your main category query, then work out which of them you can legitimately appear in. That list is your off-page plan for the year. The full two-prompt audit method is in LLMO explained.
What about AI SEO, AIO and the rest?
AI SEO and AIO are ambiguous labels. AI SEO usually means using AI tools to do SEO work, which is a workflow question, not a visibility one. AIO sometimes means AI Overview optimization and sometimes AI Optimization. Both terms carry too little agreed meaning to be useful in a strategy document.
I would drop both from your vocabulary. When a vendor pitches “AI SEO,” ask which of the two things they mean. The answer tells you a lot. I go further on why this ambiguity costs you in a contract in AI SEO and AIO are not real categories.
Two other terms are worth keeping, because they name real work that the acronym wave has obscured:
Entity SEO is optimizing your brand as a node in a knowledge graph rather than as a set of pages. Consistent naming, a filled Wikidata entry where you qualify, sameAs links across profiles, and Organization schema with real identifiers. This underpins both GEO and LLMO and almost nobody does it properly. The full setup, sameAs links, Organization schema and Wikidata, is in Entity SEO: build your brand as a knowledge graph node. The schema side of this is covered in the schema markup guide.
SXO (Search Experience Optimization) is matching page type to intent and making the post-click experience deliver on the promise. It matters more now, not less. When AI answers absorb the informational queries, the clicks that still reach your site are higher-intent, and a mismatched page wastes a more valuable visitor.
GEO vs AEO vs LLMO vs AI SEO: side by side
Four sections of definitions is a lot to hold in your head. Here is the same information in one view.
| Term | Stands for | Real definition behind it? | Where the work happens | How you measure it |
|---|---|---|---|---|
| GEO | Generative Engine Optimization | Yes — originates from a published academic study | On pages you control | Citation share in generative answers |
| AEO | Answer Engine Optimization | Partly — industry coinage, no single source | On pages you control | Snippet and answer-box capture |
| LLMO | Large Language Model Optimization | Partly — but describes genuinely different work | On pages you do not control | Whether models describe your brand correctly |
| AI SEO | No fixed expansion | No — marketing label | Varies by whoever is selling it | Undefined |
| AIO | AI Optimization / AI Overviews | No — ambiguous, means two different things | Varies | Undefined |
Read the table and the pattern falls out. GEO and AEO differ in surface, not in craft. LLMO is the genuine outlier — it is the only one where the work happens off your own site. The last two are labels attached to existing work.
Each term has a full breakdown of its own:
- What is GEO? The study behind the term
- AEO vs GEO: what actually changes when you format for answers
- LLMO explained: getting your brand described correctly
- Why AI SEO and AIO are not real categories
Which crawlers you actually need to allow
AI crawler access is the technical foundation of every AI search strategy. If GPTBot, ClaudeBot, PerplexityBot, OAI-SearchBot or Google-Extended are blocked in robots.txt, those systems cannot read your content, and no amount of content formatting will produce a citation.
There are two categories, and conflating them causes most of the confusion.
Training crawlers collect content for model training. GPTBot (OpenAI, announced August 2023), ClaudeBot (Anthropic) and Google-Extended (Google, September 2023) sit here. Blocking these is a legitimate business decision. Some publishers block them deliberately and are right to.
Retrieval crawlers fetch pages in real time to answer a live user query. OAI-SearchBot, PerplexityBot and Claude’s search agent sit here. Blocking these removes you from AI answers directly. There is almost no case where that serves a commercial site. The full crawlability picture, including these bots, is in the technical SEO guide.
Check your own robots.txt today. In several client audits I have found blanket AI blocks added by a plugin default or a nervous developer, with nobody aware the site had opted out of AI visibility entirely.
What to do about it: open yoursite.com/robots.txt right now and confirm no Disallow rule targets OAI-SearchBot or PerplexityBot. Fix it before you touch any content.
The llms.txt question
llms.txt is a proposed file, similar in spirit to robots.txt, that would give AI systems a curated markdown index of a site’s important pages. It was proposed in September 2024. As of the latest public statements from Google, no major AI system reads it.
John Mueller of Google said plainly in June 2025 that no AI system currently uses llms.txt, and compared it to the keywords meta tag. Server log checks by multiple practitioners have found the file simply is not requested. I go through the full picture, including why I still build one anyway, in llms.txt: what it is and whether it matters yet.
So why do I still generate one for clients? Because it costs an hour, it forces a useful conversation about which twenty pages actually matter, and if adoption arrives the file is already there. That is a low-cost bet, not a ranking factor.
Do not let anyone sell you llms.txt as an AI visibility service. Expected impact: low. Confidence: high, based on Google’s direct statement.
Where the acronyms genuinely diverge
Here is the position I will defend: GEO and AEO are not separate disciplines. They are the same craft applied to two surfaces, and treating them as separate services is a pricing decision rather than a technical one.
LLMO is different, and it is the one most sites are neglecting. Everything in GEO and AEO happens on pages you control. LLMO happens on pages you do not control. It is public relations, community presence, review management and directory accuracy, judged by whether a model can describe your business correctly. That is a different team, a different budget and a much longer timeline.
If you are deciding where to spend this quarter, here is the order I would use:
- Crawler access. Impact: high. Effort: one hour. Priority: critical. Nothing else works if this is broken.
- Passage structure on your top 20 pages. Impact: high. Effort: two weeks. Priority: high. Definition blocks, sourced statistics, self-contained sections.
- Entity consistency and Organization schema. Impact: medium. Effort: one week. Priority: high. Cheap and durable.
- Off-site presence in the sources AI systems cite. Impact: high. Effort: ongoing. Priority: medium. Slowest to move, hardest to fake, worth the most once it lands.
- llms.txt and other proposed standards. Impact: low. Effort: one hour. Priority: low.
Measurement is the part nobody has solved. Google Search Console does not separate AI Overview impressions cleanly. GA4 will show you referral traffic from chatgpt.com and perplexity.ai, which is real but undercounts the zero-click citations that never send a visit. Manual prompt tracking, run monthly against a fixed list of twenty queries, is currently the most honest measurement available. It is tedious and it is better than the dashboards.
Best practices for AI search optimization
The priority order above tells you what to do first. This is how to do each thing without wasting the effort.
Open the door before you decorate the room
Check your robots.txt and your firewall rules for AI crawler user-agents before you touch a single page. A blocked crawler makes every other item on this list worthless, and blocks get added by security plugins and CDN rules without anyone deciding to add them.
This takes an hour. It is the only item here where the downside of skipping it is total.
Answer in the first forty words
Put a direct, self-contained answer immediately under each heading, before the context and the caveats. Forty to eighty words. No pronouns pointing back at earlier paragraphs.
The reason is mechanical, not stylistic. Answer systems select passages, not pages. A passage that depends on the paragraph above it cannot be lifted cleanly, so it does not get lifted.
Make every section survive on its own
Read any single section of your page in isolation. If it makes sense without the rest of the article, it is citable. If it starts with “as we saw above,” it is not.
This is the single most common failure I see on otherwise strong content.
Attach a source to every number
Statistics with a named source and a date get quoted. Bare numbers get skipped, and unsourced numbers actively damage you when a model retrieves a competing figure that does have a citation attached.
If you cannot source it, cut it.
Be specific about who you are
Named author, real credentials, consistent entity details across the site. Ambiguity about who published something is a reason for a model to prefer a source where that is unambiguous.
Fix how you are described elsewhere
This is the LLMO half, and it is the half most sites skip because it sits outside the CMS. Directory accuracy, review presence, mentions on sites you do not own. A model’s description of your business is assembled from those sources, and you cannot edit it by publishing another blog post.
What to stop doing
- Buying GEO and AEO as separate retainers. Same craft, two surfaces. Paying twice is a pricing outcome, not a technical requirement.
- Writing for word count. Length has no relationship to citability. Self-contained passages do.
- Treating llms.txt as a ranking lever. Covered above — it does not do what most vendors imply.
- Abandoning conventional SEO. A page that cannot be retrieved cannot be cited. Retrieval is still the gate.
- Measuring this with rank trackers. Position is the wrong unit. Citation share is the right one.
Key takeaways
- GEO and AEO are the same craft on two surfaces. Treat them as one workstream.
- LLMO is the genuine outlier because the work happens off your own site.
- AI SEO and AIO have no fixed definitions. Ask anyone selling them what they actually mean.
- Crawler access is the gate. Nothing else matters if it is shut.
- Passage structure beats page length, because answer systems select passages.
- Conventional SEO still decides whether you are in the candidate set at all.
Common questions
Is GEO replacing SEO?
No. Every AI answer system retrieves from an index before it generates. Ranking well is still how you get into the candidate set. GEO adds a layer on top of SEO rather than replacing the foundation.
What is the difference between GEO and AEO?
AEO targets a single extracted answer, like a featured snippet or a voice response. GEO targets inclusion as one of several cited sources inside a synthesised AI answer. The content techniques overlap almost completely.
Does schema markup help with AI citations?
It helps AI systems parse your content accurately, which supports selection. Google has not confirmed schema as a direct AI Overview ranking input. Treat it as a clarity mechanism with medium confidence, and implement Organization, Article, FAQ and Product schema where they genuinely apply.
Should I block AI crawlers?
Only the training crawlers, and only if you have a specific commercial reason such as licensing your content. Never block retrieval crawlers like OAI-SearchBot or PerplexityBot on a site that wants customers.
How do I track AI visibility?
Combine three sources: GA4 referral traffic from AI domains, GSC for the queries where AI Overviews appear, and a manual monthly prompt log across your top twenty commercial questions. No single tool covers it yet.
Do I need a separate GEO agency?
No. Any competent SEO team that understands passage structure, entity clarity and crawler access can do this work. Ask a prospective vendor to explain the difference between a training crawler and a retrieval crawler. The answer will tell you whether they know the field.
Written by Kavinder Singh, SEO & Digital Marketing Strategist. Last updated: August 5, 2026.