In One Sentence
LLMO (Large Language Model Optimization) refers to optimization tactics for getting your company cited by LLMs (ChatGPT / Claude / Gemini, etc.). It is roughly synonymous with GEO and is another label used especially in non-Japanese literature.
What does this look like in practice?
For example, when reading an overseas SEO blog, you might see something like:
"LLMO is the new SEO. Optimize your content for large language models like GPT-4 and Claude."
This points to essentially the same concept that is called "GEO" in Japan.
The actual tactics are the same:
- Get Schema.org structured data in order
- Deploy llms.txt
- Build out FAQ pages
- Publish first-party information
How to distinguish GEO / AEO / LLMO
| Term | Origin | Emphasis | Where it is mainly used |
|---|---|---|---|
| GEO (Generative Engine Optimization) | Generative AI "engines" | Optimization for the whole search experience | Japan / North America |
| AEO (Answer Engine Optimization) | "Answer" engines | Appearing in the direct answer to a question | Traditional SEO sphere |
| LLMO (Large Language Model Optimization) | The "LLM" itself | Optimization for the model | Overseas, especially among engineers |
Why you should know it
- When reading overseas literature: There are areas where LLMO appears more frequently
- When doing competitor analysis: Overseas tools (such as Profound) are sometimes written from an LLMO perspective
- When writing technical blogs: For engineering audiences, LLMO sometimes resonates better
Which should you use?
In Japanese marketing contexts GEO is the standard. GEO Meter also adopts GEO as its primary term.
That said, in technical documentation and overseas-facing communication, using LLMO alongside it broadens recognition (much like SEO, multiple labels coexist at this stage).