LLMO
LLM Optimization is the broad practice of making content readable, indexable, and citable by large language models. It is the umbrella term that contains GEO and AEO.
Where does LLMO fit against GEO and AEO?
LLMO is the superset. It covers three distinct signals: training-data signals, which decide whether your content was in the model's pretraining corpus; retrieval signals, which decide whether your content surfaces in a live RAG pipeline at answer time; and citation signals, which decide whether you are actually attributed when your content gets used.
Most businesses only need to care about retrieval and citation signals at first, because those are the ones you can influence on a useful timescale. Training-data inclusion is slow and largely out of your hands, but retrieval and citation respond to the same work that drives GEO and AEO: clean structure, strong entity signals, and a presence on the sources engines pull from.
Thinking in LLMO terms keeps you from over-indexing on a single tactic. A brand can have perfect schema and still be invisible if no trusted third-party source mentions it, and vice versa. The job is to cover all three signals, which is exactly how GrowthManager scopes a visibility program.
When teams use the LLMO frame to plan work, the budget conversation gets sharper. Training-data work is the slowest-moving lever, usually requiring authoritative third-party coverage that compounds over quarters. Retrieval work moves on a weekly cadence and benefits from technical fixes, fresh content, and crawler access. Citation work depends on how a page is written and structured to be extractable. Most programs that stall do so because they over-invest in one signal and ignore the other two. A balanced LLMO program publishes weekly, audits monthly, and pursues earned coverage continuously, with measurement that distinguishes which signal moved each result.
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