Definition
Large Language Model Optimization (LLMO) is the practice of influencing what large language models know, retrieve and say about a brand, product or topic. It targets the model rather than a search engine: the aim is for ChatGPT, Claude, Gemini and similar assistants to describe you accurately and include you when users ask about your category, whether or not the answer cites a URL.
The term is slightly older than GEO. One of the earliest recorded uses is a December 2022 blog post by Jina AI titled 'SEO is Dead, Long Live LLMO', written weeks after ChatGPT launched, which proposed LLMO for getting a business mentioned in LLM answers. Olaf Kopp used LLMO in a Search Engine Land article in October 2023, a month before the GEO paper appeared, and has described LLMO, GEO and GAIO as terms that emerged around the same time. No single person or paper owns it, and Google's May 2026 guidance on generative AI search names AEO and GEO but not LLMO.
Where GEO and AEO are framed around engines and answers, LLMO is framed around the model's knowledge. That brings two layers into scope:
- The parametric layer is what the model absorbed during training. Facts that appear consistently across many independent sources are more likely to be learned, and changes only arrive with new models. OpenAI lists an April 30, 2026 knowledge cutoff for GPT-6 Astra, so anything published later is unknown to it without search. This layer is covered in more depth under training data optimization.
- The retrieval layer is what the model reads when it searches the web during a conversation. Here LLMO shares GEO's playbook: crawlable pages, coverage of the query fan-out sub-queries, and passages that state facts plainly.
The distinction is useful for diagnosis. If an assistant gets your brand wrong with web search turned off but right with it on, the problem is in the parametric layer and will take a model generation to fix. If it is wrong even with search, the pages it retrieves are the problem, and you can usually find and fix them.
Typical LLMO work focuses on entity clarity. Use the same name, category, founding facts and product names on your own site, in directory and review profiles, in press materials and, where the brand is notable, in Wikipedia and Wikidata. Strong entity salience in independent coverage helps a model associate the brand with the right category. When a model states something false, trace the claim to its likely sources (old articles, retailer listings, forum threads) and correct them, since an AI hallucination about a brand often repeats an error that exists somewhere on the web.
LLMO is measured by how often and how accurately models mention the brand across a fixed prompt set, often reported as share of model per assistant, with separate runs for search-enabled and memory-only answers. Each major model release resets the baseline. Our guide to fact-checking what AI says about your brand walks through the correction process.
In day-to-day use, LLMO, GEO and AEO share most of their methods and many teams treat them as synonyms. LLMO is the clearest of the three when the question is what a model believes about you, not where a page ranks.
Examples of Large Language Model Optimization (LLMO)
- A fintech founded in 2021 finds that Claude, answering without web search, calls it a crypto exchange because early press coverage used that label. The team updates its About page, Crunchbase profile and Wikidata entry and pitches corrected descriptions to two trade publications, then tracks whether newer models pick up the change.
- A marketer runs 'best payroll software for agencies' in ChatGPT with search on and off. The brand appears only with search on, which tells her the gap is in training data rather than in her pages.
- A hardware maker standardizes product names across retailers, manuals and press kits after assistants keep confusing its two router lines, which had been listed under different names in different markets.
- An LLMO program reports share of model per assistant each month and treats every major model release, such as GPT-6 in ChatGPT in October 2026, as a new baseline rather than comparing across versions.
