LLMO, GEO, AEO and AIO: Understanding AI Optimization Terms

LLMO, GEO, AEO and AIO are all names connected with helping AI answers find your company. Much of the work overlaps, but the targets and scope are not always the same.

Suppose Company A proposes “LLMO” and Company B proposes “GEO.” Do you need two separate efforts, or are they the same? The names alone do not tell you. This article covers the criteria for comparison, what GEO and AEO mean, where the four terms overlap and differ, and how to compare proposals.

Comparing LLMO, GEO, AEO and AIO: target, origin and scope

Compare by three criteria rather than the label: target AI, origin of the term and work included. Aligning these lets you place differently named proposals in the same table.

• Target AI: Google Search’s AI features only, or also ChatGPT and Gemini
• Origin of the term: who defined it and how, such as a research paper, tool provider or glossary
• Work included: questions tested, items reported, pages changed and how results are tracked

Providers define these terms and their scope differently. In your own documents, writing one line such as “We call this X and cover Y” keeps discussions aligned.

What GEO means: getting selected as a source in generative AI answers

GEO (Generative Engine Optimization) is a name for work that helps your content become more visible as a source in answers generated by AI.

The term was introduced in the paper “GEO: Generative Engine Optimization” by researchers at Princeton University and elsewhere. Published in August 2024 in the KDD 2024 proceedings, it presents GEO as a framework for improving content visibility in generative engine responses [1].

Note that GEO is not about geographical location. Work to help stores appear in map searches such as Google Maps is usually called local search or map optimization (known as MEO in Japan) and is different from GEO. If a proposal says “GEO,” confirm whether it targets AI answers or map search.

What AEO means: getting used in direct answers to questions

AEO (Answer Engine Optimization) is a name for work that helps AI answers that respond directly to questions reference or feature your company.

HubSpot’s glossary describes AEO as structuring content so that answer engines such as ChatGPT, Gemini and Perplexity surface it when generating direct responses [2]. It says success is measured by how frequently and accurately a brand appears in AI answers, regardless of clicks [2].

HubSpot also sells AEO tools. Use its definition as a reference, but expect the work to overlap heavily with LLMO and GEO. Before commissioning AEO separately, check whether it duplicates work in existing proposals.

Comparing overlaps and differences with LLMO and AIO

The work behind the four terms is largely shared, but AIO alone can mean Google’s AI Overviews. Internally, choose one name and add the target AI.

Term	Focus	Origin or example definition	Watch out for
LLMO	Answers generated by large language models (LLMs)	Semrush defines it as improving a brand’s visibility and portrayal in LLM responses [3]	Defined by a tool provider
GEO	Answers generated by AI	Introduced in a research paper [1]	Do not confuse with map search optimization
AEO	Direct answers to questions	Defined in HubSpot’s glossary [2]	Work overlaps with LLMO
AIO	AI in general, or AI Overviews	Some Japanese tools use it for AI Overviews [4]	Has two meanings

Mieruca SEO’s AIO Report (AIOレポート) shows how often Google’s AI Overviews appear for registered keywords [4]. A label saying AIO does not guarantee that ChatGPT research is included. The two meanings of AIO are explained in What Is AIO?

The shared work is nearly identical under any label: choose customer questions, record AI answers, fix gaps on public pages and recheck under the same conditions. Google says there are no additional requirements for appearing in AI Overviews or AI Mode and that SEO basics apply [5]. Whatever the label, keeping pages usable in search is the foundation.

Comparing proposals with different names on the same scope

Compare proposals in a table that aligns target AI, number of questions, measurement method and who makes changes. The table below is a template; the entries for Companies A and B are hypothetical.

Item	Company A (LLMO)	Company B (GEO)
Target AI	ChatGPT, Gemini	Google’s AI features only
Number of questions	20	Not stated (to confirm)
Tests per question	3 each	1 each
Items reported	Name mentions, links, cited sources	Whether you appeared
Official reports used	None	Search Console Generative AI report
Who changes pages	Your company	Company B drafts, your company fact-checks

Filling in the table reveals points such as “different names, overlapping work” or “same name, different targets.” Do not fill blank fields with guesses; keep them as items to confirm.

In the measurement row, also confirm whether official reports are used. Search Console’s Generative AI report rolled out to all websites on August 31, 2026, and shows impressions in Google’s AI features in separate Search and Discover reports, by page, country and date, with a device breakdown for Search only [6]. Clicks are not among the announced items [6].

Bing Webmaster Tools’ Citation Share, launched in preview on June 16, 2026, shows your site’s share of all citations across all sites for a specific grounding query, the search query the AI uses to find supporting sources [7]. Microsoft describes it as an observational metric, not a ranking or traffic share [7]. If a proposal promises to “raise citation share,” ask which metric it means.

Finally, choose one internal name. Example: “We call this LLMO and cover ChatGPT and Google’s AI features.” LLMO and how to start are explained in What Is LLMO?, and the link with the old name SGE in SGE Is Now AI Overviews.