Answer Engines Optimization
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AEO Glossary

LLM Optimization (LLMO)

LLM Optimization (LLMO) is the work of improving how large language models represent a brand: the facts they associate with it, the contexts where they mention it and the sources they cite about it, both from trained knowledge and from retrieval.

Large language models answer from two layers: what they learned during training and what they retrieve at answer time. LLMO addresses both. The trained layer changes slowly and is shaped by the public corpus that describes a brand over years; the retrieval layer can change in weeks when pages, structured data and external sources improve.

What can realistically be influenced

  • Retrieval-time evidence: crawlable pages, clear claims, structured data and consistent profiles, where changes show up fastest.
  • Entity disambiguation: making sure the model can tell the brand apart from similarly named entities.
  • The public corpus: legitimate mentions, reviews, documentation and coverage that future training runs and current retrieval both consume.
  • Not controllable: forcing a specific answer or removing variance between sessions.

LLMO is mostly used as a synonym of AEO and GEO. When a distinction is drawn, LLMO emphasizes the model representation of a brand, while AEO emphasizes appearing in user-facing answers. For working programs the activities converge: measure answers, fix entity facts, publish citable evidence, earn external validation.

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