What is LLMO
Large Language Model Optimization, and how it relates to AEO and GEO
Definition
LLMO (Large Language Model Optimization) is the practice of making a brand or information source appear and be cited inside the answers generated by large language models such as ChatGPT, Gemini, Perplexity, and Claude.
Where SEO aims to rank inside a list of results, LLMO aims to be part of the answer itself. As more users finish their task inside the generated answer without clicking a link, ranking in a list no longer guarantees exposure. LLMO closes that gap.
LLMO is near-synonymous with AEO (Answer Engine Optimization), GEO (Generative Engine Optimization), and GSO (Generative Search Optimization). In practice the work is the same; only the naming perspective differs.
LLMO vs AEO vs GEO vs GSO
All four name the same phenomenon from a different angle.
Named after what is being optimized for — the language model itself. Dominant in the Japanese-speaking market.
Named after the output format — the answer rather than the result list. Dominant in English-speaking markets.
Named after the nature of the engine — that it generates. Coined in a 2023 academic paper and common in research contexts.
Named after the act of generative search. Used less often than the other three.
Terminology is not yet settled, so the same work appears under different names. This site standardizes on "AEO" for consistency. See What is AEO for the underlying mechanism.
Which term does each market use
In Japanese, LLMO is searched far more than AEO. Monthly search volumes measured with Google Ads Keyword Planner in August 2026:
| Query (Japanese) | Monthly searches |
|---|---|
| LLMO とは (what is LLMO) | 4,400 |
| LLMO 対策 (LLMO measures) | 2,900 |
| AEO とは (what is AEO) | 1,300 |
| AEO 対策 (AEO measures) | 720 |
Source: Google Ads Keyword Planner, August 2026, measured by Sighted.
Neither term is more correct. They are simply the labels that stuck in different markets.
What LLMO work actually involves
LLMO is not about writing magic phrases that models like. It is about making your information retrievable, summarizable, and worth citing when a model assembles an answer.
Model answers vary between runs, so a single check proves nothing. You need repeated trials to capture the variance statistically.
Unlike SEO keywords, the prompts users actually type are never exposed. You hypothesize the relevant question set and narrow it through observation.
Generic explanations already exist in abundance, so a model has no reason to cite yours. Put the numbers, pricing, process, and criteria only you have into the body text, not just metadata.
Models extract fragments. Pages mixing several topics lose information during summarization.
A page that AI crawlers cannot fetch never becomes a candidate. Check robots.txt, rendering strategy, and response time.
Common misconceptions
Incorrect. Most assistants consult an existing search index when generating answers, so a page that search engines have not indexed does not reach the model either. LLMO sits on top of SEO rather than replacing it.
Structured data aids understanding, but what gets quoted is the body text. Facts that exist only inside JSON-LD rarely surface in an answer.
There is no fixed ranking inside a generated answer. Measure mention rate and citation rate across repeated trials instead of a single position.
How to measure LLMO
The metrics are the same as AEO: Mentions, Citations, Placements, Response Share, and Referral Traffic. Definitions are documented in What is AEO.
Sighted observes mentions and citations across ChatGPT, Gemini, and Perplexity daily, captures the variance statistically, and traces which URL caused the change.
Read next
- What is AEO — how answers are generated, and the metrics
- Knowledge hub — implementation, strategy, analysis
- Glossary — AI search terminology