What is LLMO (LLM Optimization)?

LLM Optimization is the practice of shaping how large language models describe and recommend your product. It’s another label for the same work as AEO and GEO — the acronym just emphasizes the models themselves.

The short definition

LLMO (Large Language Model Optimization) is the practice of making sure large language models — ChatGPT, Claude, Gemini, and the models behind Perplexity and AI search — understand your product and recommend it accurately when users ask buying questions. The name puts the spotlight on the LLM as the thing you’re optimizing for.

LLMO vs AEO vs GEO

These three acronyms describe the same discipline from different angles — the industry simply hasn’t settled on one word yet:

AEO (Answer Engine Optimization) — emphasizes the answer the user sees.
GEO (Generative Engine Optimization) — emphasizes the generative engine.
LLMO (LLM Optimization) — emphasizes the underlying language model.

Don’t get hung up on the label. The work is identical: clean structured data, an llms.txt, content written around real buyer questions, and a consistent description of your product across the web.

What LLMO actually involves

Because models build recommendations from what they can read and trust, LLMO comes down to engineering those readable signals so an answer engine can confidently name you. Then you measure progress with share-of-answer — the percentage of buyer prompts where the model mentions you.

Should you call it LLMO?

Use whichever term your audience uses. We default to AEO because “answer engine” captures what the buyer experiences — a single recommended answer, not a list. But if your team thinks in terms of the models, LLMO points at exactly the same playbook.