AEO vs GEO vs LLMO: three names, one discipline
Almost nothing in practice. AEO (answer engine optimization), GEO (generative engine optimization) and LLMO (large language model optimization) all describe the same work: making AI systems mention, cite and recommend your business. The emphasis differs slightly — AEO frames it around answers, GEO around generative search, LLMO around the models' training data — but the levers are identical: mentions, freshness, retrievable answer-first content, and the technical floor. Pick one term, do the work.
What is the difference between AEO, GEO and LLMO?
The terms, side by side:
AEO — answer engine optimization — The answer surfaces (ChatGPT, AI Overviews, Perplexity) — Being inside the composed answer
GEO — generative engine optimization — Generative search experiences — Citations in AI-generated results; the academic literature uses this one
LLMO — large language model optimization — The models themselves — Presence in training data; brand-topic association
AI SEO / AI search optimization — Marketing shorthand — Umbrella label buyers actually type
Where the terms came from
GEO entered through academic work on generative engines and stuck in research circles. AEO grew out of the SEO industry reaching for a name buyers understand — answers instead of rankings. LLMO emphasises the training-data half: being so consistently mentioned across the web that models associate your brand with your category before any retrieval happens. Vendors occasionally manufacture distinctions between them to sell three services; the distinctions do not survive contact with the actual work.
The one real distinction worth keeping
Inside the discipline there are genuinely two mechanisms, and the AEO/LLMO split gestures at them: retrieval (an assistant fetches live pages at question time — winnable in weeks with ranking, readable, answer-first content) and memory (the model's baked-in associations from training data — moved over months by accumulated mentions). Every serious program works both; naming them separately is useful, invoicing them separately is not.
Which term should you use?
Whichever your audience uses — and measure what people actually type: "AEO" leads in commercial search, "GEO" in research contexts, and plain "how do I show up in ChatGPT" beats both combined. We brand the practice AEO and answer to all of it. The vocabulary will consolidate on its own; the businesses that win meanwhile are the ones doing the work under any name.
Is GEO different enough from AEO to hire separately for?
No. Any vendor selling GEO and AEO as separate line items is billing you twice for one deliverable. The audit, the content system, the mention pipeline and the measurement are the same regardless of the acronym on the invoice.
Does LLMO mean I can optimize what's in the model's training data?
You cannot edit what a model already learned, but you influence what the next training runs learn: the public web's accumulated mentions of your brand. That is the long game of every mention you earn — it compounds into the models themselves over time.
Will these acronyms even exist in two years?
The work will; the labels will consolidate the way "web marketing" became SEO. Build the durable assets — mentions, answer content, measurement — and the terminology can sort itself out without costing you anything.
What does VX-N actually sell under this name?
One AI visibility program: the audit, the technical floor, answer-first content on a monthly refresh, mention outreach, per-platform prompt measurement — plus paid placement inside ChatGPT as the separate, clearly-labelled fast lane. Whatever acronym you arrived searching for, that is the work.
Last reviewed 23 August 2026