What does LLM optimization mean?
LLMO puts the focus on the model. A large language model is the AI system behind ChatGPT, Claude, Gemini and similar assistants. LLMO asks: when someone asks the model about your category, does it know you, describe you correctly and link to you?
The name is not standard. Wikipedia lists large language model optimization (LLMO) as one of several names for generative engine optimization, and says usage has "no consensus definition" (Wikipedia, read 1 November 2026). So check what someone means when they use it.
How does an LLM learn about your brand?
There are two routes, and the AI companies run separate crawlers for each:
- Training data. OpenAI says GPTBot "is used to make our generative AI foundation models more useful and safe" (OpenAI, read 1 November 2026). Anthropic's ClaudeBot collects content that could contribute to training (Anthropic, read 1 November 2026). What a model learns this way only changes when the model is trained again.
- Search at answer time. OpenAI says OAI-SearchBot "is used to surface websites in search results in ChatGPT's search features". Anthropic runs Claude-SearchBot and Claude-User. PerplexityBot is "designed to surface and link websites in search results on Perplexity" and "is not used to crawl content for AI foundation models" (Perplexity, read 1 November 2026). These answers change as the web changes.
For a B2B SaaS brand, the second route is where most of the work pays off. It is faster, and it gives the user a link they can click.
Why does LLMO matter for B2B SaaS?
G2's 2026 survey of 1,076 B2B decision-makers found that 51% of B2B software buyers now start their research in an AI chatbot more often than in Google. If the model describes your product wrongly or leaves it out, the buyer may never visit your site.
How do you do LLM optimization?
- Decide per crawler what you allow. Blocking a search crawler can remove you from that assistant's search answers.
- Describe your company the same way everywhere: what you sell, for whom, and where you are based.
- Write pages that answer one question each, in plain sentences a model can quote.
- Get described correctly on third-party pages that models cite: lists, reviews, community threads.
- Test a fixed set of prompts on each model, several times, every month.
Repeat the tests because answers vary. In Prefer's September 2026 study of 960 answers, the same question asked twice shared 96% of cited sites on Perplexity but 37% on ChatGPT and 35% on Gemini. ChatGPT cited 3.05 sources per answer on average, Perplexity 19.48.
What does LLMO look like? (hypothetical example)
A hypothetical example: a project management tool asks Claude, ChatGPT and Gemini "what is [product name]". Two of them describe a feature the company removed last year. The team updates its own about page, fixes its profiles on two review sites, and asks one blog to update an old review. A month later it asks the same question again, three times per model.
LLMO vs GEO, AEO and AI SEO
In practice they overlap almost fully. AI SEO is the broad label, GEO and AEO focus on the answer, and LLMO focuses on the model. Google, for its own AI features, calls all of it "still SEO" (Google Search Central).
How does LLMO connect to third-party lists and AI citations?
Models lean on other people's pages. Foundation and AirOps found that on unbranded questions only 2.24% of AI citations went to the brand's own pages. So an AI citation for your brand usually comes from a list or review that names you. The full playbook is in LLM SEO: how B2B SaaS brands get named in AI answers.