LLMO(Large Language Model Optimization)
Also called: LLM optimization, LLM SEO
Definition
LLMO, short for Large Language Model Optimization, aims to improve the way large language models know, understand and cite a brand. It covers two things: what the model learned during training, and what it looks up on the web when it answers.
In practice, LLMO, GEO and AEO describe very similar work, and the vocabulary hasn't settled yet. LLMO puts the focus on the model itself: what it knows about you without searching, and the mistakes it repeats when the information available about your brand is old or contradictory.
Example
If a model says your software costs €29 a month when your pricing changed a year ago, that's an LLMO problem: what it learned no longer matches reality.
What it means for your AI visibility
A model answering from memory doesn't visit your site. What it says about you depends on what has been circulating on the web for months: reviews, comparisons, articles, Wikipedia. LLMO is about making that information accurate, consistent and easy to pick up.
Related terms
- LLMAn LLM is an AI model trained on huge volumes of text to understand and produce language. It is the engine behind ChatGPT, Claude or Gemini.
- GEOGEO covers the practices that make a brand visible in the answers of generative AI such as ChatGPT, Gemini, Claude or Perplexity.
- AEOAEO means optimizing content for engines that give a direct answer: featured snippets, voice assistants and generative AI.
- Knowledge cutoffThe knowledge cutoff marks the end of a model's training data. Whatever happened after it escapes the model, unless it searches the web.
- HallucinationA hallucination is false information presented confidently by an AI: an invented price, a feature that doesn't exist, a fake quote.
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