GEO vs. AEO vs. LLMO: The Complete Guide — How to Use Each Term

Executive summary

• AEO (Answer Engine Optimization) ≈ LLMO (Large Language Model Optimization) are roughly synonymous — both are about optimizing what the Answer Engine / LLM picks as a "source / reference."
• GEO (Generative Engine Optimization) sits on a different axis: it focuses on getting your company or product mentioned by name when a generative AI composes the body of its answer.
• Their origins differ as well: AEO is older SEO vocabulary (from around 2015), LLMO emerged around 2023, and GEO is the most recent concept, originating in a 2024 Princeton paper.
• In Japan, GEO is the dominant term. But in observation terms, it pays to track two separate axes — "GEO" (name mentions in the answer body) and "AEO ≈ LLMO" (citation as a source URL).
• The industry often mixes the three as synonyms, but this article uses a breakdown faithful to each term's etymology.

1. Definitions and origins of the three terms

Each term reflects a different view of "what should be optimized for."

The starting point of AEO

Around 2015, Google began aggressively rolling out the Answer Box (later called Featured Snippet) that surfaces a "direct answer" at the very top of the search results page. Around the same time, voice search via Alexa and Siri began to spread, kicking off the zero-click trend where "users receive only the answer, without clicking the search results."

AEO is the set of measures aimed at this shift. FAQ structuring (FAQPage schema), concise answer-first body structure, and clear heading hierarchies — all of them target the goal of "making Google more likely to lift the content into an answer box."

The starting point of LLMO

After ChatGPT launched in late 2022, a new question emerged: what information do LLMs (large language models) pick when generating an answer? LLMs essentially use information contained in their training data, plus external information ingested through RAG (Retrieval-Augmented Generation).

LLMO is the concept of being selected as part of the LLM's "internal knowledge" or as a retrieved source. The term began appearing at overseas SEO conferences around 2023.

The starting point of GEO

In 2024, researchers at Princeton University published a paper titled "Generative Engine Optimization", which had a strong influence on the industry. The paper statistically examines methods that improve the probability of being cited inside generative AI search (Perplexity, Bing Chat, ChatGPT, etc.).

The term "Generative Engine" was powerful enough to capture an entirely new form of search engine, and GEO quickly became the industry's mainstream term.

2. What is shared

All three ultimately aim for the same goal:

Reach a state where your information gets "cited," "selected," or "used as the answer" by AI (generative AI / LLMs / search engines).

Most of the recommended tactics are shared as well:

• Implementing structured data (Schema.org)
• A FAQ-style body that clearly maps "question → answer"
• An answer-first structure (the conclusion in the first 200 characters)
• AI crawler guide files such as llms.txt
• Signals of authority (first-party data, explicit sources, regular updates)
• Observation and continuous improvement across multiple AI platforms

In other words, from a practitioner's standpoint, the tactics are the same regardless of which term you use.

3. Slightly different focuses — what to look at

The nuance, in one line each:

The etymology makes the mapping simple:

• Generative Engine = an engine that "generates the answer itself" in natural language. ChatGPT, Claude, and Gemini are typical examples — they mention companies and products by name inside the answer body.
• Answer Engine = an engine that returns "the answer (and source URLs)" to a question. Perplexity and Google's Answer Box are typical examples — the focus is on choosing the source URLs.

GEO Meter follows this etymology and observes on two axes:

• Mentions inside AI answers (the AI calls you "by name") → the GEO viewpoint (Generative Engine oriented)
• Source domain ranking (the AI presents a URL as a "source") → the AEO ≈ LLMO viewpoint (Answer Engine / LLM source selection)

Within the same topic, "companies mentioned by name" and "companies cited by URL" can diverge, which is why tracking both pays off.

4. How to use each term in practice

When talking to customers or internal stakeholders

"GEO" is the safe term to standardize on. It is the dominant term in Japan, the newest, and the most encompassing. Treating AEO and LLMO as "specific viewpoints contained within GEO" is the elegant move.

When reading overseas literature

• If you see AEO → it is the older SEO-industry context, discussing Answer Box / Voice Search.
• If you see LLMO → it is an LLM-vendor-leaning context (OpenAI / Anthropic), discussing training data or RAG.
• If you see GEO → it is the most recent research / comprehensive discussion.

When choosing observation metrics

Keeping the two axes provided by GEO Meter in mind makes prioritization easier:

• Zero name mentions (weak GEO viewpoint) → The generative AI is not including your name in the answer body. The priority is to build "reasons to be named" via FAQ structuring, first-party data publishing, and PR.
• Zero URL citations (weak AEO ≈ LLMO viewpoint) → The Answer Engine / LLM finds you hard to pick as a source. The priority is to become a "machine-readable source" via Schema.org, llms.txt, and a Wikipedia entry.
• Zero on both → Start with the highest-leverage measure: setting up llms.txt (GEO Meter observed a +30pp gap from this alone) to open the doorway into both Engine types.

5. Frequently asked questions

Q. Is AEO already an old term?

A. It is still in active use within the SEO industry, but in the generative-AI era, GEO is more comprehensive, so for new articles, GEO communicates more cleanly.

Q. What's the difference between LLMO and GEO?

A. LLMO focuses on "being embedded into the LLM's internal knowledge or training data," while GEO focuses on "the output of the generative AI." In practice they are nearly synonymous; overseas literature tends to use LLMO, while Japan leans heavily on GEO.

Q. Do I need to memorize all three?

A. GEO alone is enough for daily work. For AEO and LLMO, it's enough to "recognize the meaning when you see them in literature."

Q. Does SEO become unnecessary?

A. No. The authority you have built through SEO (inbound links / domain authority) carries over to GEO. It is more accurate to view GEO as optimization on a separate axis from SEO, not as a replacement for it.

6. Summary

• Etymologically, the field splits into two lineages: Generative Engine = GEO (mentions in the answer body) and Answer Engine = AEO ≈ LLMO (selection of source URLs).
• In practice, GEO is the standardized term in Japan. AEO ≈ LLMO is the same concept under different labels.
• GEO Meter observes on the two axes of name mentions (GEO) and URL citations (AEO ≈ LLMO) and proposes measures for both. The two can diverge, and one-sided strength is common.
• The direction of tactics — "set up llms.txt," "add Schema.org," "build FAQ structure" — is shared across all three terms. Don't get swept up in vocabulary; what matters is running the observe-then-improve cycle.

Related links

• What is GEO? — Short glossary entry
• What is AEO? — Short glossary entry
• What is LLMO? — Short glossary entry
• GEO Basics: The Complete Guide — The big picture of GEO and the state of the Japanese market
• GEO Implementation: The Complete Guide — Concrete implementation steps