LLM optimization (LLMO)
Published by Krister Ross · Updated July 2026
ChatGPT, Gemini and Claude have already formed an opinion about your brand. LLM optimization — LLMO — is the discipline of shaping that opinion: what the models know, what they say, and whether they recommend you.
In short
LLM optimization (LLMO) is the work of influencing how large language models describe, assess and recommend your brand. Models form their picture of you from training data and live retrieval — so LLMO works on both: making the web's description of you consistent and authoritative, and making your own content easy to retrieve and cite. LLMO overlaps with AEO and GEO; the distinct focus is the model's underlying knowledge of your brand as an entity.
What is LLM optimization?
A large language model does not look your brand up in a database. It has a compressed picture of you, learned from everything written about you across the web — and it fills gaps with retrieval when it answers.
That picture can be accurate, outdated or empty. LLMO is the practice of auditing what models believe about you, correcting the sources that shaped it, and feeding the ecosystem the consistent, verifiable facts you want the next model version to learn.
The stakes rise every year: model answers increasingly decide which brands get considered at all. What the model believes about you is becoming your de facto reputation.
The three channels LLMO works through
Everything a model knows about you arrived through one of these.
Training data
What was written about you before the model's cutoff: media coverage, directories, reviews, your own site. Consistent, factual coverage across many sources becomes the model's baseline belief about you.
Live retrieval
When models search the web at answer time, your current pages compete to be retrieved and cited. Answer-shaped content and schema markup decide whether your pages are usable as sources.
Entity signals
Knowledge graphs, structured data and identical facts everywhere teach models who you are as an entity: name, category, offer, location. Contradictions here make models hedge — or skip you.
The LLMO work list
Audit: ask the major models what they know and recommend in your category
Correct: fix outdated or wrong facts at the sources models learned them from
Strengthen: publish clear, citable content on the questions that matter
Structure: schema markup and consistent entity data across the web
Earn: coverage in the publications and directories models trust
Monitor: track mentions, sentiment and citations across model versions
Frequently asked questions about LLMO
The questions we hear most often.
Large Language Model Optimization — the work of influencing how models like ChatGPT, Gemini and Claude describe and recommend your brand, through both their training data and live retrieval.
They overlap heavily. AEO optimizes for being the answer in AI search, GEO for visibility in generative engines, and LLMO focuses on the model's underlying knowledge of your brand as an entity. In practice one program covers all three.
Yes, on two horizons: live retrieval reacts within weeks when your content and citations improve, and the model's baseline picture updates when new model versions train on the improved web footprint.
Measure it: run your category's key questions through the major models on a schedule and track mentions, descriptions, sentiment and citations over time. CitationLab AI Monitor automates exactly this.