What is LLMO?
LLMO, large language model optimization, is the practice of making your business known to and retrievable by the models behind ChatGPT, Claude, Gemini, and Perplexity. The term came from the developer side of the field, which thinks in models rather than search products, and that framing highlights something the other acronyms blur: a model can know about you from training, retrieve you live at answer time, or both, and the two paths reward different work.
What’s the difference between training and retrieval?
Training is what a model learned when it was built; retrieval is what it looks up while answering. Training-side presence comes from being in the datasets labs use, with Common Crawl’s CCBot as the classic gatekeeper, and it pays off diffusely: models across the ecosystem have baseline knowledge of you.
Retrieval-side presence comes from ranking in the search indexes models query live, and it pays off immediately as citations with links. The balance of power is lopsided. Ahrefs’ 1.4M-prompt study shows ChatGPT’s answers leaning overwhelmingly on live search retrieval, which is why you can’t train-data your way into answers about anything current.
What does LLMO work look like in practice?
Mostly the same foundation as every other acronym, applied with the model’s constraints in mind. LLM retrieval systems are crude parsers: they don’t run JavaScript, they strip markup to plain text, and they’re impatient about latency, so your substance has to live in fast, raw HTML. They judge pages by title, snippet, and URL before opening them.
And they lift passages rather than pages, favoring the first 100 words, where Surfer measured almost 40% of AI citations originating. The LLM visibility checker tests exactly these constraints, including a real fetch with an AI-crawler user agent to catch firewall blocks.
Which crawlers gate your LLM visibility?
Five, each feeding different systems: GPTBot for OpenAI, ClaudeBot for Anthropic, PerplexityBot, Google-Extended as the control token for Gemini, and CCBot for the Common Crawl dataset. Each obeys robots.txt in its own group, and all of them can be overridden by your CDN, since Cloudflare has blocked AI crawlers by default on new domains since mid-2025.
The full tour of who reads what, and the two-layer access problem, lives in how AI crawlers work.
Can you optimize what a model says about your brand?
Indirectly, and only through what it can read. Models synthesize from retrievable sources, so the brand answer you get reflects the pages that rank for questions about you, the consistency of your details across the web, and reviews, which function as a second check before a model recommends anyone.
What you cannot do is inject preferences directly; there is no meta tag for “describe us favorably,” and the file that claims to be one goes unread. The honest lever is publishing the clearest retrievable answer to every question your market asks, then keeping your entity details consistent enough that models don’t hedge.
How does LLMO relate to AEO and GEO?
Same discipline, different lens. AEO looks at the answer surface, GEO at generative search products, LLMO at the models underneath, and the acronym guide maps all four. LLMO’s distinct contribution is the training-versus-retrieval distinction above, which occasionally changes a real decision, for instance whether blocking CCBot matters to you.
For everything on-page, run the free scan once and you’ve measured what all four vocabularies care about.
See where your site stands. The free scan takes about fifteen seconds and shows every fix.
Check your LLM visibility free