Large language model optimization
Examines how a brand, entity or body of information is understood and represented across model-powered experiences.
Do not let the acronym become the strategy.
LLMO is large language model optimization. GEO is generative engine optimization. AEO is answer engine optimization. Each describes part of the work required to make information clear, retrievable, credible and useful in generated answers.
A business rarely needs three disconnected programs. It needs one coordinated approach across technical accessibility, entity clarity, answer-ready content, credible evidence, search visibility and repeatable AI-response measurement.
Generative engine optimization
Emphasizes visibility, citations and inclusion in generative search or answer experiences that synthesize web information.
Answer engine optimization
Structures content so direct questions can be answered clearly and accurately by search and assistant experiences.
Search engine optimization
Improves crawlability, relevance, authority and organic search visibility. It remains foundational, even as the interface changes.
Practical questions about LLMO, GEO and AEO
The best starting point is the buyer decision and evidence gap—not allegiance to one term.
Which term should we use internally?
Use the term your stakeholders understand. “AI visibility” is often the clearest executive category, while LLMO, GEO and AEO can describe specific practices inside the program.
Should we stop doing SEO?
No. Crawlable pages, useful content, clear information architecture and earned authority remain important. AI visibility adds new outputs and measurements; it does not erase search fundamentals.
Is AEO just writing FAQs?
No. Clear question-and-answer sections can help readers and machines, but answer readiness also depends on source quality, entity consistency, page accessibility, context and evidence.
What should we do first?
Define the commercial questions and markets that matter, establish a repeatable baseline, diagnose content and evidence gaps, then prioritize improvements that help both buyers and discovery systems.