# LLMO (large language model optimization)

> A third name for the same work as GEO and AEO. What the term adds, what it does not, and the one distinction that is actually load bearing.

Source: https://armature.tech/library/llmo
Published: 2026-09-03
Publisher: Armature, Inc. (https://armature.tech)

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> **LLMO (large language model optimization)** is the practice of making content that large language models use and represent accurately. It describes the same work as generative engine optimization and answer engine optimization, with the emphasis on the model rather than the engine or the answer.

## The three acronyms

| Term | Emphasis | What it measures |
| --- | --- | --- |
| AEO, answer engine optimization | Being extracted as the answer | Answer presence, citations |
| GEO, generative engine optimization | How the engine represents you | Mentions, share of voice, sentiment |
| LLMO, large language model optimization | How the model represents you | The same, framed around the model |

Underneath, one discipline. Clear structure, direct answers, current specific facts, third party corroboration, and content a crawler can actually fetch.

If somebody sells you AEO, GEO and LLMO as three services, you are buying one thing three times.

## What LLMO adds

One useful framing, in fairness to the term.

AEO and GEO are about a retrieval step: a system fetches pages and builds an answer. LLMO points at something slower and larger, which is what the model learned during training.

Those two are different in a way that matters for planning.

**Retrieval** you can move in weeks. Publish a better page, and a system that searches will find it.

**Training** you move over years, by being written about consistently across the web, by many sources, in the same way. You cannot ship it in a quarter and you cannot buy it.

For a developer tool this distinction has a concrete consequence. In our measurements, Claude Code ran a web search in 1.6% of decision runs. In 98% of its decisions it was working from what it already learned. That is the training side, and no amount of publishing this month reaches it.

## What none of the three capture

All three assume a person reads the output.

A coding agent inside a repository reads no answer and clicks no citation. It reads the codebase, decides what the request needs, sometimes searches, installs a product and writes the integration. The output is a diff.

That surface behaves differently in ways that break the acronyms entirely:

- The same hosted database request produced one winner in 111 of 111 JavaScript sessions and 48 of 132 TypeScript sessions. The repository decided, and no acronym above contains a repository.
- A written instruction naming a preferred product lost 2 times out of 3 once a rival library was installed.
- The three coding agents disagreed about the category leader in 10 of 18 categories.

That is [agent discoverability](/library/agent-discoverability), and it is measured in installs rather than mentions.

## The practical advice

Use whichever term your audience uses. Argue about the acronyms as little as possible.

Then make sure you are measuring both surfaces, because the naming argument is a distraction from the fact that most companies are measuring one of them and calling it AI visibility.

## Common questions

### What is LLMO?

LLMO, or large language model optimization, is the practice of making content that large language models use and represent accurately. It describes the same work as generative engine optimization and answer engine optimization.

### Is LLMO different from GEO?

Not meaningfully. The three acronyms emphasise different parts of one discipline: LLMO the model, GEO the generative engine, AEO the extracted answer. The tactics are the same and the differences are mostly in what gets measured.

### Should I use the term LLMO?

Use whichever term your audience uses. The word matters less than the distinction between optimising for a person reading an answer and optimising for an agent installing software, which none of the three acronyms capture.

### What is the distinction that does matter?

Whether a person reads the output or an agent acts on it. LLMO, GEO and AEO all assume a person reads. A coding agent installing a package reads no answer and clicks no citation, and that surface needs its own measurement.

## Read next

- [Generative engine optimization (GEO)](https://armature.tech/library/generative-engine-optimization) (Markdown: https://armature.tech/library/generative-engine-optimization.md)
- [Answer engine optimization, explained](https://armature.tech/library/answer-engine-optimization) (Markdown: https://armature.tech/library/answer-engine-optimization.md)
- [AEO vs GEO vs SEO, and the fourth one nobody names](https://armature.tech/library/aeo-vs-geo-vs-seo) (Markdown: https://armature.tech/library/aeo-vs-geo-vs-seo.md)
- [Agent discoverability: the complete guide](https://armature.tech/library/agent-discoverability) (Markdown: https://armature.tech/library/agent-discoverability.md)

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