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Glossary

Large Language Model Optimization (LLMO)

Large Language Model Optimization (LLMO) is the practice of making content easy for a large language model to retrieve, understand, and repeat accurately, so it names your business when answering a customer's question.

Also known as: Large Language Model Optimization

Large language model optimization is the newest of three overlapping labels for the same idea: getting an AI system to name your business when it answers a customer's question. The other two are GEO (generative engine optimization) and AEO (answer engine optimization). Each vendor tends to pick a favorite term, but nobody in the industry has drawn a real line between them. Treat GEO, AEO, and LLMO as synonyms.

In practice, LLMO work looks like: publishing pages that state your service area, pricing, and licensing in plain language; keeping your listings on directories and review sites current; and giving a model enough real, specific detail (project photos, actual price ranges, actual response times) that it has something concrete to repeat back.

It's worth being honest about how much of this is measurable. Profound's research across 100,000 prompts found that ChatGPT and Perplexity overlap on only 11% of the domains they cite, with 37.4% of citations being ChatGPT-exclusive and 51.6% being Perplexity-exclusive. That means optimizing for one large language model does not automatically help you with another. Any LLMO effort has to account for each engine separately.

One claim you'll see in LLMO marketing that isn't backed by evidence: that adding an llms.txt file to your site improves how models treat your content. It doesn't, as of mid-2026 no major AI system consumes that file. See the llms.txt entry for the full story.