SEO vs. LLMO vs. GEO
Why Classic SEO Is No Longer Enough for AI Systems
For many years, the logic of the web was relatively clear.
Anyone who wanted to be visible on Google optimised their content for search engines. Keywords, backlinks, technical performance and clean metadata determined which pages were found.
But something fundamental is shifting.
More and more people are no longer asking questions through a traditional search. They ask AI systems directly: ChatGPT, Gemini, Copilot, Perplexity.
The search is evolving from a list of links to a direct answer.
This raises a new question for organisations: Are our contents written in a way that an AI can actually understand them?
This is exactly where new disciplines like LLMO (Large Language Model Optimization) and GEO (Generative Engine Optimization) come in.
The Difference Between SEO and LLMO
SEO and LLMO share a similar goal: making content visible. But they optimise for different systems.
SEO optimises content for search engines. Typical factors include keywords, backlinks, technical performance, meta tags and internal linking. The goal is a good ranking in search results. Users then decide which page to click on.
LLMO optimises content for language models. Here, it’s less about ranking and more about clarity and interpretability. Key factors include clear statements instead of marketing buzzwords, structured content, precise terminology and complete answers to user questions.
GEO vs. LLMO – Two Perspectives on AI Visibility
Another term frequently appears in discussions about AI visibility: GEO, short for Generative Engine Optimization.
GEO describes the optimisation of content for systems that generate answers rather than showing links – systems like ChatGPT, Google Gemini, Microsoft Copilot or Perplexity.
The challenge for organisations: How does our organisation even get mentioned in these answers?
GEO focuses on whether a website is cited by AI systems, whether a company is mentioned in answers, and whether a brand is used as a source.
LLMO takes a different approach. It’s less about visibility and more about interpretability of content. The central question is: can a language model actually understand the content correctly?
SEO, GEO and LLMO Compared
| Discipline | Focus |
|---|---|
| SEO | Ranking in search engines |
| GEO | Visibility in AI-generated answers |
| LLMO | Comprehensibility for language models |
A simplified way to put it:
SEO brings users to a website. LLMO ensures that AI understands the content. GEO ensures that AI uses the content.
The Problem with Many Websites
Many websites have grown organically over time. They contain long texts, marketing language, unclear structures and scattered information.
A typical sentence on a corporate website: “We develop innovative solutions for sustainable business success.” It sounds good. But it says practically nothing.
A clearly formulated sentence: “Our software automates customer enquiries in customer service.” That sentence is understandable for humans – and for AI alike.
A Practical Example from Public Administration
As part of a major website relaunch, the Federal Statistical Office (FSO) in Switzerland is developing new content structures and editorial guidelines. The new content guidelines are strongly oriented towards principles that are also relevant for LLMO: clear, precise language, structured content, precise terminology and short, understandable sections.
AI as a Writing Assistant for Editorial Teams
To support editorial teams, an AI-assisted writing tool was developed. The system was trained on all existing website content, content guidelines, federal language regulations and editorial directives.
The AI therefore knows not only the organisation’s content, but also the rules by which texts should be structured.
Editors can use the AI in two modes:
Refinement Mode: An existing text is reviewed for clarity, structure and compliance with content guidelines, then revised accordingly.
Generation Mode: The AI creates an initial draft based on a given topic, target audience and content element – already compliant with defined guidelines. Editors then refine it further.
Why This Approach Is Also Relevant for LLMO
The real effect lies in the consistency of content. When all texts are created according to the same structural principles, they are clearly formulated, well-structured and comprehensive – exactly the properties that AI systems value most.
Conclusion
SEO remains important. But the way people search for information is changing fundamentally.
We used to browse websites. Then we searched on Google. Now we increasingly ask questions directly to an AI.
A new skill is becoming essential: formulating content in a way that AI systems can correctly understand it.
Organisations that structure their content clearly and write consistently will have a decisive advantage – not just in search engines, but in AI-generated answers too.