What is LLM Optimization (LLMO)?
LLM Optimization (LLMO) is the discipline of shaping the training-data substrate and retrieval surfaces that determine how large language models represent a business.
LLM Optimization (LLMO) operates one layer deeper than GEO or AEO — it focuses on the substrate of information LLMs draw from. By engineering presence across open web sources, authoritative directories, and structured knowledge bases, LLMO shapes how a model 'thinks of' a business before any prompt is even asked.
Training data vs. retrieval
LLMs blend baked-in training knowledge with real-time retrieval. LLMO addresses both — ensuring the entity exists in the model's parameters AND that retrieval surfaces reinforce that entity at query time.
Where LLMO compounds
Wikipedia, Wikidata, GitHub, Reddit, authoritative directories, academic citations, news outlets — these are the substrate sources LLMs over-index on. LLMO engineers presence across them.
GEO targets the moment of generation; LLMO targets the substrate the generation draws from. They compound.
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