LLMO
Also known as: LLM Optimization, Large Language Model Optimization
LLMO (Large Language Model Optimization) is a term close in meaning to GEO, describing work that helps a brand appear accurately and visibly in answers produced by large language models such as ChatGPT, Claude and Gemini.
LLMO names its target directly: large language models. In practice it is often used as a synonym for GEO and AEO, and nobody has drawn a firm line between them yet.
What LLMO covers
Some people use LLMO a little more broadly. It covers not only answers generated with live web search, but also what a model learned from its training data. So there are really two questions:
- Does the model know you when it answers from memory, without searching?
- When it does search the web, does it find your site and cite it?
The first is hard to influence in the short term. Training data is refreshed periodically and isn’t transparent. The second responds more directly to crawlable content, structured data and a site that’s open to AI crawlers.
Why it matters
A model that gives outdated or wrong information about you can do more harm than one that doesn’t mention you at all. Part of LLMO is spotting those errors (hallucinations) and making the correct information easy to find.
Example
If a model asked about a manufacturer still lists a product line the company dropped five years ago, current product pages and consistent company details across the web help correct that over time.