LLMO vs GEO vs AEO: Why Naming Matters in the New Era of SEO

Search is changing fast. As generative engines like ChatGPT, Perplexity, and Google’s AI Overviews reshape how people find and consume information, a new kind of optimization has emerged. But the industry can’t seem to agree on what to call it.

You’ll hear names like:

• LLMO – Large Language Model Optimization
• GEO – Generative Engine Optimization
• AEO – Answer Engine Optimization

Sometimes even AI SEO or LEO (LLM Engine Optimization).

So, what’s the difference?

In short: mostly semantics, but the framing matters.

• LLMO focuses on how content is structured so that large language models (LLMs) can find, understand, and reuse it. It’s about being machine-readable, embedding-friendly, and semantically clear.
• GEO leans into where your content appears – in the answers of generative engines. It’s performance-oriented: Are you being cited? Are you showing up in AI-driven responses?
• AEO puts the emphasis on who: optimizing for answer engines (like ChatGPT or Gemini), often in the context of chat UX or voice interfaces.

Same space, different angles. Think of LLMO as the technical layer, GEO as the performance layer, and AEO as the interface layer.

Why It Matters Now

This isn’t just another SEO acronym. The shift to AI-first search is a break from keyword-based optimization.

In traditional SEO, the question was: “What keywords do I need to rank for?”

In LLMO/GEO, the question becomes: “What information do I need to structure so that a model embeds it in its response?”

Keywords don’t drive relevance here — embeddings do.

LLMs represent content in high-dimensional vectors. If your content is ambiguous, unstructured, or shallow, it won’t land in the right vector space. That means no retrieval, no citation, and no presence in AI answers.

What the Research Actually Says

In their 2024 paper, Aggarwal et al. define GEO as optimizing content so that LLMs are more likely to retrieve and cite it in generated answers. They show that embedding-aware edits to structure and phrasing can increase citation rate by up to 40%.

Another key point: models don’t cite based on backlinks or keyword density. They cite based on vector similarity in embedding space. If your content isn’t semantically distinct and well-structured, it’s invisible.

What This Means for SEO Teams

If you’re a CSO or global SEO lead, here’s the takeaway:

• You’re no longer just optimizing for crawlers — you’re optimizing for models.
• Structured, clear, authoritative content will outperform fluff every time.
• Traditional SEO metrics (rankings, CTR) are being joined by new ones (AI mentions, retrieval rate, citation likelihood).

The stack is changing. And so is the playbook.

Whether you call it LLMO, GEO, or AEO, the core idea is the same: if you want to be seen in AI search, write like a machine is reading.