AEO vs. GEO vs. LLMO: What Marketers Need to Know
AEO, GEO and LLMO describe different parts of AI search visibility. Here is how they differ, how they work together, and what marketers should prioritize.
Jeremy Prasetyo
Co-CEO, Lanesra AI
AI search has created a new vocabulary for marketers: AEO, GEO, and LLMO. The terms overlap, but they are not identical. Each describes a different part of the same visibility problem: how your content and brand are understood, retrieved, cited, and recommended inside AI-powered discovery.
The mistake is treating them as three separate marketing channels. You do not need three content strategies. You need one evidence system that works across search engines, generative search, and conversational AI.
The short answer
AEO makes information answer-ready. GEO makes it source-ready. LLMO makes your entities and brand easier for language models to interpret consistently. They extend SEO rather than replace it, and the strongest strategy supports all three at once.
1. AEO, GEO, and LLMO in One Table
| Term | Main goal | Where it matters | Optimization focus |
|---|---|---|---|
| AEO | Become part of the direct answer. | Answer engines, AI search, question-led search experiences. | Clarity, structure, direct answers, useful evidence. |
| GEO | Become a source generative systems can use and cite. | AI Overviews, AI Mode, generative search and synthesized answers. | Depth, authority, freshness, citations, source quality. |
| LLMO | Help language models understand your entities and brand consistently. | Conversational LLM experiences and long-form generated responses. | Entity clarity, terminology, brand consistency, original insights. |
2. What Is AEO?
Answer Engine Optimization focuses on making information easy to use in a direct answer. That means clear definitions, question-led sections, concise explanations, comparison tables, structured facts, and content that resolves a specific user need quickly.
AEO grew out of answer-first search experiences such as featured snippets and voice search, but the idea is now broader: AI search systems can synthesize answers from multiple sources, not just extract one paragraph from one page.
OpenAI, for example, says ChatGPT can search the web and return answers with relevant source links and citations. OpenAI
3. What Is GEO?
Generative Engine Optimization focuses on visibility inside generative search systems. The term was formalized in academic research that described generative engines as systems that gather and synthesize information from multiple sources to answer queries. GEO research paper
For marketers, GEO means making your content credible enough to become part of the evidence layer behind a generated response. Depth matters. So do original research, clear sourcing, current information, first-party expertise, and consistency across the web.
Google says AI Overviews and AI Mode can use query fan-out, issuing multiple related searches across subtopics and data sources before building a response. Google Search Central
4. What Is LLMO?
Large Language Model Optimization is an industry term for improving how language models understand and reference a brand, entity, product, person, or concept.
Unlike GEO, LLMO is not a standardized technical framework. It is better understood as a useful label for entity and brand optimization inside conversational AI: consistent naming, clear relationships between entities, authoritative descriptions, structured information, and enough external validation for models to understand what your brand is and where it belongs.
This is especially important when users ask open-ended questions such as “What companies are best for X?”, “Who are the leading experts in Y?”, or “What is the difference between A and B?”
5. How They Work Together
One content system, three optimization layers. The labels differ, but the work compounds.
This is why one strong page can support all three. A concise definition near the top can help AEO. Deeper analysis and cited evidence can strengthen GEO. Consistent entity language and brand context can strengthen LLMO.
6. Which One Should You Prioritize?
| If your priority is... | Start with... | Why |
|---|---|---|
| Winning direct question-led searches | AEO | Clarity and answer structure matter most. |
| Being cited in generative search | GEO | You need stronger evidence, depth, and source credibility. |
| Improving how AI describes your brand | LLMO | Entity consistency and brand context become critical. |
| Building durable AI visibility | All three | The systems overlap too much to optimize in isolation. |
7. SEO Is Still the Foundation
None of this makes SEO irrelevant. AI systems still need discoverable, crawlable, understandable information. Technical quality, internal linking, useful content, structured data, clear authorship, and trustworthy pages remain important.
The difference is what happens after discovery. Traditional SEO asks whether the page can rank. AI visibility asks whether the information can be retrieved, trusted, synthesized, cited, and turned into a recommendation.
SEO gets you discovered. GEO helps AI systems understand and use your evidence. AEO helps you become part of the answer. LLMO strengthens how models understand the entities behind that answer.
The Lanesra View
At Lanesra, we treat these terms as layers of one AI visibility system rather than separate disciplines. The practical objective is the same: understand how AI search sees your brand, which sources support it, where competitors are winning, and what needs to change.
The marketers who win will not be the ones who memorize the most acronyms. They will be the ones who build a consistent evidence footprint across the web and measure how that footprint performs across AI engines.