AEO vs GEO vs LLM SEO: what's the difference?

Mostly, yes. AEO, GEO and LLM SEO all describe the same core job: getting your content retrieved, understood and cited when an AI system writes an answer instead of showing ten links. The labels come from different places and stress different parts of that job, but the tactics overlap almost completely.

Google takes the strongest version of that view. Its guide to generative AI search addresses the acronyms directly and says that from Google Search's perspective, optimizing for generative AI search is optimizing for the search experience, and thus still SEO. Microsoft is more comfortable with the new vocabulary: Bing describes its AI Performance report as an early step toward generative engine optimization tooling.

So the useful question is not which acronym is right. It is which parts of the work your business is neglecting, and how you will know if it is working. The rest of this guide defines each term, shows what is genuinely different, and gives a comparison table you can use when evaluating vendors.

Answer engine optimization is the practice of structuring content so that a system returning one answer, rather than a list of links, picks your page as its source. The term was in use before ChatGPT, when the targets were featured snippets and voice assistants such as Alexa and Siri. It now covers Google AI Overviews and AI Mode, ChatGPT search, Perplexity, Copilot and Claude.

AEO's emphasis is question-first content: pages that match a specific question, answer it in the opening sentences, and support the answer with steps, tables and FAQs. Its typical metric is answers and citations won for a defined list of questions. Our pillar guide on answer engine optimization covers the full process, step by step.

Generative engine optimization was named in a research paper, 'GEO: Generative Engine Optimization', by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande. It was first posted to arXiv in November 2023 and accepted to KDD 2024. The authors described generative engines as systems that search and then synthesize one answer from several sources, and proposed GEO as a way for content creators to improve their visibility inside those answers. They also built GEO-bench, a benchmark of queries across many domains.

The paper tested nine ways of rewriting a source page. Adding citations to credible sources, adding quotations and adding statistics performed best, and the authors report visibility gains of up to 40% on their benchmark. Keyword stuffing, the classic SEO move, often performed worse, and results varied by domain. Treat those numbers as results from a controlled experiment, not a forecast for your site, but the direction is useful: evidence beats keywords.

In marketing use, GEO now means roughly the same as AEO, with more attention on multi-source answers, share of voice across engines, and the off-site sources those engines draw on.

LLM SEO, sometimes written LLMO for large language model optimization, is the broadest of the three labels. It includes everything in AEO and GEO, plus two things they tend to skip. The first is how a model describes your brand from what it learned in training, with no search at all. Ask a chatbot with web search turned off what your company does and you see the model's memory, shaped by what was written about you before its training cutoff. The second is agent access: making your site easy for AI agents that fetch documentation or complete tasks, which is where llms.txt files and clean markdown versions of pages come in.

The phrase LLM search optimization is used in two different ways, so check which one someone means. Usually it means optimizing your content for LLM-powered search engines such as ChatGPT search or Perplexity. Occasionally it means building search into your own product with a large language model, such as semantic site search or a support assistant grounded in your documents. The second is a software project, not marketing, and is closer to retrieval-augmented generation than to SEO.

Strip away the labels and every serious practitioner works on the same foundations. If a vendor's AEO, GEO or LLMO plan skips any of these, it is incomplete, whatever it is called:

• Crawler access: search engines and AI search crawlers such as OAI-SearchBot, PerplexityBot and Claude-SearchBot can reach the pages, and the content is in server-rendered HTML.
• Indexing: pages are in Google's and Bing's indexes, because Google's AI features and many assistants retrieve from search indexes.
• Question-matched pages: one focused page per real buyer question, with the answer in the opening sentences.
• Entity consistency: the same name, description and facts on your site, in your structured data and on third-party profiles.
• Evidence: original data, case studies, named sources, quotations and specific numbers that a model can attribute.
• Off-site presence: accurate mentions in reviews, directories, comparison articles and communities the engines read.
• Measurement beyond rankings: AI impressions, citations, AI referrals and a recurring prompt panel.

The differences are real but narrow, and they are mostly about emphasis and measurement rather than tactics.

AEO thinks in single questions and single answers. Its unit is the question you want to own, and its output is a page structured to be quoted. GEO thinks in multi-source answers. It assumes the engine will blend several sources and asks how much of the final answer comes from you, which is why GEO tools talk about share of voice and position within the generated response. LLM SEO thinks about the model itself: training-data presence, how the model describes you unprompted, and agents that read your site directly.

There is a fourth term people confuse with all three: AI SEO. That usually means using AI tools to do SEO work faster, such as clustering keywords, drafting briefs or auditing internal links. It is a production method, not a target. A team can use AI SEO tools to do AEO, or do AEO with no AI tools at all.

Measurement is where the labels diverge most in practice, because each points at a different signal. AEO work is usually judged on a fixed list of questions: does an answer engine answer them using your page, and with a link? Google Search Console's Generative AI performance report now shows impressions in AI Overviews and AI Mode by page, which is the closest first-party view for Google.

GEO work is judged on share of generated answers across engines and prompts. Bing Webmaster Tools' AI Performance report shows total citations, cited pages and the grounding queries that Copilot and Bing used to retrieve your content. For ChatGPT, Perplexity and Claude, teams run prompt panels by hand or with monitoring platforms that sample prompts at scale.

LLM SEO adds brand-description checks: asking models with search disabled what your company does, who it serves and how it compares, then tracking whether the answers become more accurate over months. None of these metrics is standardized yet. Pick a small set, define it before you start, and keep it stable so you can see change.

Use whichever term your audience uses. Searchers type answer engine optimization, GEO and LLM SEO for the same need, and a page or proposal can use all three as long as it defines them. What matters is the work behind the label.

When you evaluate an LLM search optimization platform or agency, ask four questions. What exactly will change on our site and off it? Which crawlers and indexes do you check, and how? How will you measure results, against what baseline, and how often? What will you not promise? Be wary of guaranteed placements or claims of inside access; Google states that no third-party tool has access to its internal ranking or AI systems. Monitoring platforms are useful for sampling prompts at scale, but they report the problem. Someone still has to fix the pages.

People search for LLM SEO case studies because they want proof before they spend. Many published examples are a screenshot of one chatbot answer mentioning a brand. One answer proves very little, because the same prompt can produce different answers on different days and for different users.

A credible case study shows a prompt set chosen before the work started, a baseline measurement, the specific changes made to pages and off-site profiles, and results measured the same way over several months, ideally alongside first-party data from Search Console or Bing Webmaster Tools. It should also separate correlation from cause. Answer engines change their models and retrieval often, so a lift across every brand in a category is not your win. If a vendor cannot show a baseline, a stable prompt set and the changes they made, treat the case study as marketing.