GEO and AEO describe the same underlying goal: being the source an AI system draws on when it writes an answer. AEO grew out of an older generation of search features — the featured snippet box and voice assistants reading a single result aloud — while GEO was coined for the newer generative engines that write a synthesized answer instead of extracting one passage. LLMO shows up mostly as a synonym in Japanese-language coverage of the same practice. Underneath the four letters, it is one discipline.
This article works through where each label came from, what a canonical reference source says about how they overlap, and which term is actually worth writing on your own site.
Where AEO came from
Answer engine optimization is usually explained as an extension of two earlier search behaviors: a extracted-passage box, and a spoken answer read by a voice assistant.
The extracted-passage box is a real, current Google feature. Google’s own documentation describes featured snippets as “special boxes where the format of a regular search result is reversed, showing the descriptive snippet first.” They can also appear “within a related questions group (also known as ‘People Also Ask’).” Google’s systems, not the publisher, decide which page gets pulled into the box: “Google systems determine whether a page would make a good featured snippet for a user’s search request, and if so, elevates it.” Clicking the box takes the user “directly to the section of the page that appeared in the featured snippet.”
That is the entire technical picture Google’s documentation offers. It says nothing about when featured snippets launched, nothing about voice search, and nothing about the origin of the term “answer engine optimization.” AEO’s connection to voice assistants — the idea that a spoken answer from a device reads out a single result instead of showing a list of links — is a common explanation in AEO write-ups, but it is not something Google’s page confirms or dates, so treat it as commonly repeated context rather than a documented fact. What is verifiable is narrower and more useful: AEO practice targets a single extracted passage inside a search engine’s own interface, and Google — not the publisher — decides which passage gets extracted (see the pillar guide’s AEO entry).
Where GEO came from
GEO has a cleaner paper trail. The term comes from a specific research paper: “GEO: Generative Engine Optimization” by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan, and Ameet Deshpande, first submitted on November 16, 2023, and accepted at KDD 2024. The paper frames the problem plainly: as generative engines replace traditional search results with synthesized answers, content creators have “little to no control over when and how their content is displayed.” It introduces GEO as “the first novel paradigm to aid content creators in improving their content visibility” in those generated responses, along with GEO-bench, a benchmark for testing optimization methods.
The paper’s headline result is that its tested methods “can boost visibility by up to 40% in generative engine responses” — a research finding from controlled benchmark conditions, not a guaranteed outcome for any specific website, and the paper itself notes that “efficacy varies across domains.” Treat that number as evidence that content-level changes can move visibility in generative answers, not as a promise about a particular site (see our Method page for how a result like this differs from a live guarantee).
Unlike AEO, GEO’s origin is a dated, citable document rather than a loosely attributed marketing narrative. That is one reason we use it as our own label — more on that below (see the pillar guide’s GEO entry).
What a reference source says about the overlap
Wikipedia’s article on generative engine optimization is a useful check on how much these terms actually differ in practice. It defines GEO as “the practice of structuring digital content and managing online presence to improve visibility in responses generated by generative artificial intelligence (AI) systems,” and states directly: “Other terms for the same concept include answer engine optimization (AEO), large language model optimization (LLMO), artificial intelligence optimization (AIO), and AI SEO.” The article adds that there is “no consensus definition established” among these terms as of early 2026, and that they are “frequently used interchangeably in trade and practitioner contexts.”
The article also cites two positions on how different this work really is from SEO. Google’s own stated position, as quoted there, is that “optimizing for generative AI search is optimizing for the search experience, and thus still SEO.” A Forrester Research analyst is quoted taking a more qualified view, calling AEO “significantly, but not fundamentally, different from SEO,” and suggesting that advocates sometimes overstate the gap between the two for marketing reasons.
Read together, a general-purpose reference source treats AEO as a synonym for GEO rather than a separate discipline, and treats the whole cluster of terms as a variation on SEO rather than a replacement for it.
LLMO and AIO, briefly
LLMO — large language model optimization — is the least standardized of the four terms and, in our own reading of coverage, shows up mostly as a term used in Japanese-language trade coverage rather than English-language sources; the Wikipedia article above lists it as one of GEO’s synonyms without further definition (see the pillar guide’s LLMO entry).
AIO is more ambiguous than useful, because it collides with two unrelated meanings — “AI optimization” as a broad practice, and Google’s “AI Overviews” feature, which some SEO writers abbreviate the same way. We cover that ambiguity in more depth in our AIO vs. GEO guide, including how Google’s own Search Central documentation treats AI Overviews as governed by the same established SEO practices. If you only read one of our articles on terminology, that one focuses on the AIO side; this one focuses on AEO (see the pillar guide’s AIO entry).
The real differences that matter
Even if AEO and GEO describe the same goal, three practical differences are worth knowing, because they change what you look at when you check your work.
Surface. AEO’s classic target is a snippet box: a single extracted passage, still inside the search engine’s own results page, still styled as part of that engine’s interface. GEO’s target is a generated answer: text the engine composes itself, often with inline citations to more than one source, sometimes with no visible “results page” at all.
Unit. AEO optimizes for one passage winning one box for one query. GEO optimizes for how a domain shows up across a synthesized answer that may pull from several sources at once — a citation, not a slot. A page can be a strong AEO candidate and still be absent from a generated answer that draws on different sources entirely.
Measurement. Classic AEO work is checked with rank trackers and Search Console, looking at snippet ownership per query. GEO work has to be checked against sampled generated answers instead, because there is no single ranked results page to inspect. That is a different measurement problem, and it is the one our Method page explains in detail — what a sampled answer can and cannot tell you, and how to read a score alongside its underlying questions and sources.
What to do regardless of the label
The underlying editorial work barely changes across AEO, GEO, or LLMO. In every case:
- Put a direct, complete answer near the relevant heading, not buried under a feature name.
- Make sure the page is actually reachable — indexed for classic search, and not blocked for AI crawlers that respect
robots.txt. - Back claims with evidence a reader can check, rather than marketing language alone.
- Keep facts consistent across your own pages, since an engine synthesizing an answer can draw from more than one of them at once.
None of that requires picking a side in the AEO-versus-GEO naming debate. It requires being accurate and specific, which both practices reward for the same underlying reason.
Which term should you actually use, and why
Given that a reference source treats these as synonyms, the choice mostly comes down to which term other people are actually searching for and using. We looked at two independent signals.
In Google autocomplete on September 29, 2026 (US, English), “geo vs seo” returned 10 of 10 on-topic suggestions, including “vs aeo” as one of the suggested comparisons — readers are actively comparing GEO against both SEO and AEO in the same breath. “Aeo” on its own returned only 1 of 10 on-topic suggestions, with a stock ticker and a film taking most of the remaining slots. “Llmo” fared similarly: only 1 of 10 on-topic, with autocomplete pulling toward “MLO” and “MLOps” instead. “Generative engine optimization” itself returned 10 of 10 on-topic suggestions (meaning, strategies, services, tools, agency, course). In short: GEO is the term people search around directly; AEO and LLMO mostly get typed as comparisons or get swallowed by unrelated autocomplete noise.
We also looked at where practitioners actually discuss this field in long-form technical conversation. Across the nine Hacker News threads we reviewed on this topic (mid-2025 to February 2026), every one used “Generative Engine Optimization” or “GEO” in its title; none used AIO, AEO, or LLMO.
Both signals point the same direction, which is why we use GEO as our own working label, while still explaining AEO, AIO, and LLMO for readers who arrive searching for those terms specifically.
How to check where you stand today
Terminology aside, the useful next step is a concrete check: does a domain actually show up when an AI engine answers a question about its category, and separately, what happens when the question already names the brand? The extension checks exactly that from inside Chrome, on any site you are already viewing. Once you have a result, the Method page explains how to read the two scores, the sampled questions behind them, and the cited sources, so a single number does not stand in for the whole picture.
FAQ
What does AEO mean?
AEO stands for answer engine optimization: work aimed at getting a page selected as the answer a search engine extracts and displays directly, most commonly as a featured snippet box or a voice assistant’s spoken response. It predates the current wave of generative, citation-based AI answers.
What does LLMO mean?
LLMO stands for large language model optimization. It is one of several synonyms for the same broad practice as GEO, and in our own reading of the coverage, it appears mostly in Japanese-language trade writing rather than as a standard English-language term.
Is GEO just AEO renamed?
Not exactly renamed, but closely related. A reference source we checked lists AEO as one of several synonymous terms for GEO, with no field-wide consensus on where one practice ends and the other begins. The clearer distinction is the output each one targets: a single extracted passage for AEO, versus a synthesized, often multi-source answer for GEO.
Should I use GEO or AEO when writing about this on my own site?
Our own autocomplete and forum research both point to GEO as the term people actually search for and discuss, so we use it as our primary label. AEO is still worth explaining for readers who search for it directly, since it remains a common way to phrase the comparison.
Does Google consider GEO different from SEO?
No. Google’s own stated position, as quoted in the Wikipedia article on GEO, is that optimizing for generative AI search “is optimizing for the search experience, and thus still SEO.” Treat GEO as a newer measurement lens on the same underlying work, not a separate discipline.
