LLMO: shaping how the models remember you
Large Language Model Optimization is the work of making sure that when an LLM talks about your category, it knows who you are, gets your facts right, and reaches for you as an example.
What is LLMO?
LLMO (Large Language Model Optimization) is the practice of shaping how LLMs understand, recall and represent your brand. It is concerned with the model’s internal picture of you — what it “believes” you are, what you sell, who you serve, and whether it trusts you enough to recommend you.
Two things feed that picture: training data (the web as it was when the model was built) and retrieval (pages the model fetches at answer time). LLMO works both surfaces — making your brand well-described across the open web, and making each page clean enough to be retrieved correctly.
LLMO, GEO and AI SEO
| Term | Focus | Time horizon |
|---|---|---|
| AI SEO | Overall AI visibility | Ongoing |
| GEO | Citations in live answers | Days to months |
| LLMO | How the model represents you | Months, across model updates |
The LLMO playbook
Be a consistent entity
Use one exact brand name everywhere. Link every page to a single Organization identity. Inconsistent naming splits your entity and confuses the model about who you are.
Get described by others
Models learn from third parties more than from your own marketing. Wikipedia, Wikidata, Reddit threads, directories and press coverage shape the brand picture far more than your homepage copy.
State your facts plainly
Put the unambiguous facts — what you are, who you serve, where you are based — in plain text and in structured data. Ship an llms.txt with a suggested-citation summary so the model has a clean line to reuse.
Keep facts current
Outdated claims propagate. When something changes — pricing, product, positioning — update it everywhere consistently and resubmit so retrieval picks up the truth fast.
How to check what an LLM thinks of you
- Ask several engines directly: “What is [your brand]? What do they offer?”
- Note errors, omissions and outdated facts.
- Trace each error to its likely source on the open web and fix it there.
- Re-test on a schedule — representations drift as models and the web change.
Train the machines to know you
The grimoire’s authority and entity rites are pure LLMO. 35+ rites, one-time $29, lifetime updates, 30-day Covenant.
Begin the Rite — $29Frequently asked
How is LLMO different from GEO?
GEO focuses on earning citations in live, retrieval-based AI answers. LLMO is broader and includes how a model represents your brand from training data and memory, not only from real-time retrieval.
Can I influence training data?
Not directly, but you influence what the next model learns by being well-described across the open web today — consistent naming, structured identity, and mentions on trusted sources.