Large Language Model Optimization (LLMO)
Shaping how large language models describe, mention and recommend your brand in their outputs.
Large Language Model Optimization is the work of influencing how an LLM represents your brand when it generates text. It is sometimes written as LLM optimization, GAIO, or informally as LLM SEO. The common idea is that the model itself, not a search index, is now the thing you are optimising for.
LLMO covers two sources of brand knowledge. The first is training data, the fixed snapshot the model learned from, which shapes how it describes you even with no live search. The second is retrieval, where the model fetches live sources at answer time. Different engines lean on these differently, so the same brand can be described one way by a model answering from memory and another way by a model that just searched the web.
Because you cannot edit a model’s weights, LLMO in practice means improving the public record the model reads from: consistent, accurate descriptions of your brand across the sources models trust, and correcting the places where models get you wrong.
Frequently asked questions
- Is LLMO different from GEO and AEO?
- They describe the same shift from different angles. LLMO frames the model as the optimisation target, GEO frames the generative engine, and AEO frames the answer. In day-to-day use they point at the same work.
- Can you influence what a model already learned in training?
- Not directly, since weights are fixed until the next training run. You influence the public sources the model reads so that future versions and live retrieval describe you accurately.
Related terms
Whaily turns this from theory into measurement: which brands AI names in your category, and which sources shape those answers, tracked across ChatGPT, Gemini, Claude and Perplexity.