AEO vs GEO vs LLMO, How to Get Cited in AI Search
- Harold Bell
- 3 days ago
- 9 min read
Key takeaways
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Buyers are asking AI assistants for recommendations instead of scrolling search results. Most brands are invisible in those answers even when they rank well on Google. I have spent more than 16 years in enterprise technology content marketing, and this is the first shift I have watched where a page can be doing everything right and still lose, because the thing grading it changed.
Three acronyms showed up to describe the new work. Answer engine optimization, generative engine optimization, and large language model optimization. Together they created a new category, AI visibility. And every one of them runs on different mechanics than traditional SEO.
This is the practical version. What each engine actually does, where they genuinely differ, and the starting framework I use for earning visibility where AI systems retrieve, synthesize, and cite.
How is AI visibility different from SEO
AI visibility is a distribution and citation problem, not a content quality problem. SEO optimizes a page to rank in a list. AI visibility optimizes a footprint to be retrieved and quoted. The brands winning it are optimizing for retrieval, not for rankings. |
The way B2B buyers find vendors has quietly moved out of view. Per G2’s The Answer Economy report, 51% now begin their research in an AI chatbot rather than Google. 71% rely on chatbots somewhere in the process, up from 60% a year prior.
Forrester sees the same inflection. At its 2026 B2B Summit, the firm identified AI answer engines like ChatGPT and Perplexity as replacing traditional search outright, cutting organic traffic by 10% to 40% and making AI visibility a core marketing KPI. And the trend is not bucking anytime soon. Forbes reported that 90% of B2B marketing leaders treat AI visibility as an investment-level priority.
Sit with that for a second. The first impression of your company is no longer your homepage, your ad, or your SDR’s opening line. It is a synthesized answer assembled from third-party sources, delivered in a private session, invisible to your attribution stack.
Which is why the old scoreboard stops working. I have seen client libraries where the highest-cited pages were not the highest-ranked ones, and in some cases had no top-10 position at all. If that sounds familiar, the mechanics are in how to show up in AI Overviews when you don’t rank on Google and in ranking but not cited.
What is answer engine optimization
AEO is the practice of structuring content so answer engines like AI Overviews, ChatGPT, Perplexity, and voice assistants can extract it and present it directly as the answer to a user’s question. |
Notice what that definition does. One sentence, leads with the term, answers the implied question immediately. That is not an accident. It is the format itself, demonstrated.
The mechanics diverge from classic SEO in specific ways. SEO optimizes pages to rank. AEO optimizes passages to be lifted. That requires framing content around the actual questions buyers ask rather than keyword variants, and putting the direct answer in the first sentence or two before you elaborate. I break the discipline down properly in the practical guide to answer engine optimization, and the writing pattern itself in BLUF writing for AEO.
It also means using schema markup so machines can parse what a page claims without inferring it. Google retired the FAQ rich results, but AI systems still consume the structured data underneath, which is exactly the argument in FAQPage schema and FAQ schema for AI search.
Last, it entails passage-level optimization. Every section should stand alone as a complete, quotable answer, because answer engines cite fragments, not pages. If you want a scoring rubric for that, use the AEO content audit checklist.
What is generative engine optimization
GEO is the practice of optimizing your presence for generative AI systems that synthesize answers from many sources at once, including ChatGPT, Perplexity, Gemini, and Claude. |
Where a search engine retrieves and ranks pages, a generative engine reads across dozens of sources, weighs them, and composes a single response. Your goal is not to rank in that output. It is to be woven into it. The fuller definition lives in what is generative engine optimization, and the execution sequence in GEO optimization.
These systems do not run on backlink-driven PageRank. Three variables move the needle instead.
They weigh citations, meaning whether authoritative sources reference you when discussing your category.
They weigh third-party corroboration, which gauges whether what your site claims about you matches what review platforms, analyst coverage, and industry publications say independently.
They weigh entity consistency, which determines whether your company name, positioning, and category are described the same way everywhere the model encounters you.
The takeaway is that GEO is less about your website and more about your footprint. Entity work is the highest-leverage piece for most B2B teams, and I walk through it in entity authority for B2B SaaS.
Your Google rankings also do not transfer, because the machines assembling AI answers are not reading Google’s index. ChatGPT’s retrieval runs through a hybrid of Bing’s index and OpenAI’s own crawler.
When it browses live, it queries Bing and reads the top results, which means anything invisible to Bing is invisible to ChatGPT regardless of your Google position. That makes Bing indexing a hard prerequisite rather than an optimization, and it is why Bing Webmaster Tools is the most underrated lever in AI search visibility.
What is large language model optimization
LLMO is the practice of shaping how AI models like ChatGPT and Claude represent your brand in their training data and retrieval, so they recognize, describe, and recommend you accurately. Where SEO, AEO, and GEO optimize for what engines fetch in the moment, LLMO targets what the model already believes before anyone searches. |
Yes, as if we did not have enough acronyms already. But the distinction is real and it changes the timeline of the work. You cannot submit content to a training run, so LLMO works through repetition and corroboration instead. The full definition sits in what is large language model optimization, and the technical mechanics in LLM optimization.
In practice that means publishing consistently, with entity-rich descriptions of who you are and what you do, across every surface you control. Then earning the same claims across channels you do not own. Directories, review platforms, roundups, press, communities.
Omniscient Digital’s analysis found that 57% of branded-query citations go to reviews, listicles, forums, and case studies. When your positioning, category, and framework names appear identically across dozens of independent sources, the model absorbs them as fact. This is also why your case studies matter more than your team thinks, a point I make in your B2B case studies are unquotable. And if a model is currently naming a competitor instead of you, here is why that happens.
What actually separates AEO vs. GEO vs. LLMO
AEO changes your page. GEO changes your footprint. LLMO changes the record. AEO responds in a re-crawl cycle, GEO responds in weeks to months as corroboration accumulates, and LLMO responds across model generations. Same goal, three different machines, three different clocks. |
Here is the version I put in front of clients when someone asks whether they need all four engines. They already have all four, whether they manage them or not. The only choice is whether they are deliberate about it.
Engine | What you are optimizing | Where it plays out | The signal that moves it |
SEO | A page, so it ranks in a list of results | Google and Bing organic results | Relevance, links, technical health, and index presence |
AEO | A passage, so it can be lifted whole | Featured snippets, AI Overviews, voice answers | Question-led headings, direct answers up front, and parseable schema |
GEO | A footprint, so it gets synthesized into an answer | ChatGPT, Perplexity, Gemini, and Claude responses | Citations from authoritative sources, third-party corroboration, entity consistency |
LLMO | A record, so the model already knows you | What an assistant says without browsing at all | Repetition and consistent description across sources the model trained on |
The trap is treating these as a menu. They are a sequence. AEO work makes your pages extractable, which makes them easier for generative engines to quote, which produces the citation trail that eventually feeds what the model believes. Skip the first step and the later two have nothing to work with. If you want the SEO-to-GEO version of that argument specifically, it is in GEO vs SEO.
Which engine should you start with
Start with AEO. It is the cheapest to implement, it is the only one of the three you can retrofit onto content you already own, and every improvement compounds into GEO and LLMO. Teams that start with LLMO spend a quarter on brand mentions and have nothing extractable to show for it. |
The pattern I keep running into on enterprise content programs is the same every time. The team has good content. It ranks for things. And AI engines skip it entirely, because nobody structured the pages to be liftable. That is an AEO problem and it is fixable inside one re-crawl cycle.
The retrofit order I use is boring on purpose. Confirm the page is indexed in both Google and Bing. Convert the H2s to the actual questions buyers ask. Put a self-contained answer directly beneath each one. Add the schema the template does not already emit. Then check whether the page picked up citations. Blog post format and header tags SEO best practices cover the structural half, and schema markup examples covers the parsing half.
How do you build an AI visibility strategy across all four engines
Map the four engines against the five levers that move them, then work the cells rather than the acronyms. The five levers are content, schema, distribution, authority, and citation, and each one pulls differently on each engine. |
It is time to stop funding one engine and calling it a search strategy. Four engines, four sets of mechanics, four scoreboards. A strategy built for one is a partial strategy wearing a complete strategy’s budget.
The Engine Optimization Matrix, a framework I developed, maps how these four engines respond to five levers that dictate your success.
Content, meaning what you publish and how it is structured.
Schema, meaning how machines parse it.
Distribution, meaning where it propagates beyond your domain.
Authority, meaning who vouches for you.
Citation, meaning whether third parties reference you in contexts models trust.
Four engines against five levers gives you twenty cells. That is the real unit of work, not the acronym. A team that says it is doing GEO usually means it is working two or three cells and ignoring the rest. You can see how the full matrix maps all twenty intersections, and if you need the board-level framing for why any of this matters, that is digital visibility.
How do you measure whether any of this is working
Citation rate, not rankings. Citation rate is the share of the prompts your buyers actually use where an engine names or links you. Rankings will not tell you whether AI visibility work is landing, because a page can hold its position and lose every citation. |
This is the part most programs skip, and it is the part that decides whether you get a second budget cycle. Pick the twenty or thirty prompts a real buyer would type, run them across engines on a fixed schedule, and record whether you appear, whether a competitor appears, and which source got the citation. The methodology is in how to measure citation rate and the tooling side in how to track AI search visibility.
Engines also disagree with each other more than people expect, which is useful signal rather than noise. Perplexity vs ChatGPT vs Google AI citations covers where those citation sets diverge and what each divergence tells you to fix.
Read the original This piece expands on my Forbes Communications Council article on the same subject. If you want the condensed executive version, or you want to share something with a leadership team that will not read 2,500 words, start there. |
Frequently asked questions
Is AEO just a rebrand of SEO
No. SEO optimizes a page so it ranks in a list of results. AEO optimizes a passage so an engine can lift it and present it as the answer. The two share technical foundations, but the unit of optimization is different, and so is the payoff.
Do I need all four engines
You already have all four, whether you manage them or not. The choice is whether you are deliberate about them. Most B2B teams should sequence rather than parallelize, starting with AEO because it retrofits onto content you already own.
Does my Google ranking help me get cited by ChatGPT
Only indirectly. ChatGPT retrieves through a hybrid of Bing’s index and OpenAI’s own crawler, so a page missing from Bing cannot be cited no matter where it sits in Google. Check Bing indexing before you optimize anything else.
How long does LLMO take to show results
Longer than the other engines. Retrieval-based work responds in weeks. Model memory responds across model generations, so plan in quarters and measure the leading indicator, which is how consistently third-party sources describe you.
What is the single highest-leverage change for AI visibility
Turning your H2s into the questions buyers actually ask and putting a self-contained answer directly beneath each one. It costs a retrofit pass, it works on content you already published, and it feeds every engine downstream.
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