What is LLM Optimization (LLMO)?

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LLM Optimization (LLMO) is the practice of shaping a brand’s online presence so large language models — the AI behind ChatGPT, Claude, Gemini, and others — represent it accurately and mention it favorably when they answer questions. Where search optimization targets a results page, LLMO targets the model’s own “understanding”: whether an LLM knows your brand, what it says about you, and whether it recommends you when relevant.

The premise behind LLMO is that people increasingly ask AI models directly — “what’s the best platform for B2B ecommerce?”, “which agencies build Adobe Commerce?” — and act on the answer. Those answers are shaped by what the model absorbed from its training data and what it can retrieve. LLMO works to make a brand present, correctly described, and well-regarded in that body of information, so the model surfaces it accurately.

The tactics lean on presence and consistency rather than single-page tweaks:

• Broad, consistent mentions — being referenced across the web (publications, directories, communities) so models encounter the brand repeatedly
• Accurate, structured information — clear, factual content about what the brand is and does, so models describe it correctly
• Authority and reputation — credible third-party coverage that shapes how models characterize the brand
• Entity clarity — unambiguous, structured signals about the brand as a distinct entity

LLMO matters because AI models are becoming a discovery and recommendation layer of their own — being unknown or misrepresented in them is a growing form of invisibility. It sits alongside two related practices: AEO, which optimizes for direct answers to questions, and GEO, which optimizes for being cited within AI-generated answers. The distinction is scope: AEO and GEO focus on winning specific queries and citations, while LLMO focuses on the model’s broader, more durable impression of the brand itself. All three overlap and are best pursued together as search shifts toward AI-mediated discovery.