1The short answer
LLM Optimization (LLMO, Large Language Model Optimization) refers to measures that ensure a company is represented accurately, and as often as possible, in the knowledge and answers of large language models such as ChatGPT, Gemini or Claude. The term is often used synonymously with GEO, but places more emphasis on the model's knowledge itself.
Two ways a language model knows your brand
- Trained knowledge: information the model learned from publicly available texts during training. This knowledge has a cut-off date and only changes with new model versions.
- Retrieved knowledge: information the system retrieves via a web search at the time of the question. It is up to date, but depends on which pages are found and selected.
LLMO addresses both. For trained knowledge, what counts is how consistently and how often a company is described on the web over a longer period. For retrieved knowledge, what counts is discoverability, freshness and citability – the classic strengths of Generative Engine Optimization.
What LLMO involves in practice
- Consistent core data: describe name, services, location and people the same way everywhere – see Entity Optimization.
- Clear, fact-based content that answers questions unambiguously.
- Presence on sources that are widely read and cited in your industry.
- Access for the AI providers' crawlers, if desired.
- Regular checks on how models describe the brand – including errors.
LLMO, LLM SEO and GEO
In practice, LLMO, LLM SEO and GEO are often used interchangeably. We use GEO as the umbrella term because it covers all generative search systems – including Google AI Overviews, which are based on the traditional search index. An overview of all terms can be found in the glossary.
Author
Jan Hugo · Managing Director, Search, Tracking & AI
Technical foundations, data models, tracking architecture and AI-assisted analysis.
