Is LLMO different from GEO and AEO?
Not in practice. The tasks are the same and the difference is which part of the system the name points at. If a client or a colleague uses one of the three, use theirs rather than correcting it.
All definitions
Learn
LLM optimization (LLMO)
LLM optimization, usually shortened to LLMO, is the work of making a brand more likely to be named, and described correctly, when a large language model writes an answer about its category. It is the same job that other people call GEO or AEO, named after the thing doing the answering rather than after the product it sits in.
Why there are three names for it
GEO points at the generative engine, AEO at the answer, LLMO at the model. The three describe work that is almost identical, and which one a team uses says more about where it learned the term than about what it does. LLMO tends to be used by people who think about the model itself, including its training data and what it retrieves at answer time.
What the work is
A model composes its answer from two things: what it absorbed during training, and whatever it retrieves while answering. Neither of those can be edited directly, so the work happens one step back. It means being described the same way wherever the model is likely to read about the brand, answering buyer questions plainly on pages the brand controls, and giving the facts a model needs to name it with confidence, such as what it does, who it is for and what it costs.
Why it cannot be optimized the way a ranking is
Search rewards a page and reports a position, so the loop is observable: change the page, watch the rank. A model returns prose, gives no positions, and can name a different set of brands when asked the same question twice. That variance is a property of the system rather than a defect in the measurement, and it is the reason a single answer proves almost nothing about whether the work is paying off.
How to tell whether it worked
The only reliable read is a before and after, taken the same way both times. Asked Thrice puts a brand's buyer questions to 2 model APIs, repeats each question 3 times per provider, and reports how many answers named the brand out of the total asked. Counts change slowly enough to be readable, while a single score can move for reasons that have nothing to do with the work.
FAQ
Can I optimize the model itself?
No. Nobody outside the lab can change what a model learned. What can change is what it finds about the brand when it reads and retrieves, which is why the work looks like publishing and consistency rather than tuning.
How long does it take to see a change?
Longer than a ranking change, and unevenly. What a model learned in training is refreshed on a schedule nobody outside the lab controls, while the part it retrieves at answer time can shift within days. Measuring repeatedly is what separates a real move from run-to-run noise.
Related
Other definitions
See the numbers for your own brand
The free audit reads your site, derives your buyer questions and measures them on 2 model APIs. No signup, no card.