People also ask
In practice
The naming soup — LLMO, GEO, AEO — covers one reality with different centres of gravity: LLMO speaks to the models (how LLMs retrieve, recall and represent a brand), GEO to the generated-answer surfaces, AEO to the extraction formats. The working content is shared. Retrieval: the content must be crawlable by the relevant bots and indexed where the model’s live layer searches — with the memory layer (training data, Common Crawl) a slower, separate path decided by crawl inclusion and English-language substance. Quotation: the extractable formats — direct answers, structured comparisons, quotable definitions — because models compose from passages.
Representation: the entity layer (consistent naming, sameAs, the structured record) that determines whether the model describes the product correctly when it speaks about it from memory. The distinctive LLMO emphases: model memory is testable (prompts with search disabled reveal what the model “knows” from training — the “secured” status worth knowing but not controllable on demand), and model behaviour differs per engine (source preferences diverge — community platforms here, comparison sites there — so the placement map is per-surface, not universal). The measurement and honesty standards are identical across the acronym soup: fixed prompt sets, longitudinal runs, rotation reported, presence tracked and never promised.
The practical guidance for buyers: ignore the acronym; evaluate the programme — access, extractability, entity, placement, measurement — because every serious version of LLMO, GEO and AEO is that same five-layer work.
See also: GEO, AI answer engines.
Related service: Brand mentions.
