Definition
LLMO (Large Language Model Optimization)
LLMO (Large Language Model Optimization) is the practice of making your content easy for large language models to understand, retrieve, and cite. It is largely interchangeable with AEO and GEO: all three describe getting a brand surfaced in AI-generated answers from tools like ChatGPT, Perplexity, Google AI Overviews, and Gemini.
What LLMO actually means
LLMO focuses on the systems behind AI answers, the large language models that read, summarize, and cite sources. The goal is practical: structure your content so a model can find the relevant passage, understand it without ambiguity, and reproduce your facts accurately when a user asks a related question. In practice that means clear, self-contained answers near the top of a page, consistent naming, accurate descriptions of what you do, and clean, crawlable pages. LLMO is less about gaming a ranking algorithm and more about being legible to a reader that happens to be a model summarizing the web on a user's behalf.
AEO vs GEO vs LLMO: the same idea, different labels
The acronyms cause real confusion, so here is the honest version: AEO (Answer Engine Optimization), GEO (Generative Engine Optimization), and LLMO (Large Language Model Optimization) overlap heavily and are often used interchangeably. AEO leans toward direct-answer formats and questions. GEO is the term from the original 2023 research paper and emphasizes generative engines. LLMO names the underlying models directly. The differences are mostly emphasis and who coined them, not separate disciplines. If a vendor claims one is fundamentally distinct, treat that as marketing. The shared aim is appearing accurately in AI-generated answers.
What helps, and what does not (yet)
What reliably helps: clear answer-first content, accurate structured data, consistent entity descriptions across the web, and pages AI crawlers can actually reach. What is more speculative: llms.txt has limited real-world adoption, and Google has said it does not use it, so treat it as a cheap, low-risk, forward-looking signal rather than a guaranteed citation lever. AI engines also change behavior often, so any tactic should be re-checked over time. Because AI answers cite few sources and rarely show how you are described, the practical first step is to measure your current AI visibility, which is what Laudia does.