What is LLMO (large language model optimization)?
LLMO (large language model optimization) is one of at least 4 competing names for the same discipline: making your content visible, quotable, and cited inside answers written by large language models like ChatGPT, Gemini, Claude, and Perplexity. AEO, GEO, and AIO describe the same work. Whatever the label, Google is clear there is no special format for the machines: “You don't need to create new machine readable files, AI text files, markup, or Markdown to appear in Google Search”, per Google's AI optimization guide. Our pillar guides to AEO and GEO cover the actual work.
The short definition
LLMO names the practice of optimizing content so that large language models surface it, quote it, and cite it when they answer questions. The models in question power ChatGPT, Perplexity, Gemini, Google's AI Overviews and AI Mode, and Copilot. When one of them answers a question in your niche, it leans on a small set of retrieved pages. LLMO is the work of being in that set.
The name puts the emphasis on the model. In practice, you rarely optimize for a model directly. You optimize for the retrieval pipeline in front of it: the crawlers that fetch your page, the index that stores it, and the ranking step that decides which passages the model reads. We explain that pipeline in what is RAG.
One discipline, many names
Here is the honest map. These four labels describe the same discipline with different emphasis, and none of them is the official one:
- AEO, answer engine optimization. Framed around answer surfaces: featured snippets, AI Overviews, voice assistants, chat answers. The oldest of the four labels.
- GEO, generative engine optimization. Framed around the engines that generate answers. The only label with an academic origin, a 2023 study at arXiv 2311.09735.
- LLMO, large language model optimization. Framed around the models themselves. Common in developer-leaning writing.
- AIO, AI optimization. The broadest label, and the messiest, because AIO also abbreviates Google's AI Overviews feature.
Google's own guidance names AEO and GEO as terms people use and concludes that, from Search's perspective, this work is still SEO, because the generative features are rooted in the same ranking and quality systems.
Why the naming mess exists
New acronyms are cheap and services need packaging. When AI answers started eating clicks, agencies needed a word for "SEO, but for the AI answers," and four words appeared at once. That is fine. What is not fine is treating the labels as four different services with four different price tags. If a vendor sells you LLMO on top of the GEO you already bought, ask them to name one deliverable that differs. Google publishes no LLMO spec, no AI markup, and no registration form for language models. There is nothing proprietary to sell.
Does llms.txt count as LLMO?
It gets sold that way, so let us be plain. llms.txt is a proposed file that summarizes your content for language models. It is a proposal, not a standard. Google has stated it does not use llms.txt, and no major AI engine documents the file as a requirement for citation. Adding one will not hurt you, but it will not substitute for pages the crawlers can actually fetch and read. If a vendor's LLMO deliverable is mostly a text file, you paid for a text file. We break down what the file is and who reads it in llms.txt explained.
What the work actually is
Strip the labels and the checklist is stable. Let the AI crawlers fetch your pages. Serve the answer in raw HTML, not behind JavaScript. Write self-contained passages that lead with the answer and carry specific numbers, names, and dates. Use valid typed structured data so machines can parse what the page is. Make the author and organization traceable. Then measure whether the machines actually mention you, which is what AI share of voice tracks. That checklist is the same whether you file it under LLMO, AEO, or GEO.
Go deeper
We keep the deep material under the two most common names: what is AEO for the answer-surface framing and what is GEO for the generative-engine framing and its research evidence. To see how your own site holds up against this checklist, paste your link into Brimm. We run 45 documented checks and print the failures in fix order, in plain language.