Definition
LLMO (Large Language Model Optimization) is an emerging digital marketing discipline that aims to optimize web content so it is selected, cited, and recommended by large language models (LLMs) like ChatGPT, Google Gemini, Claude, and Perplexity AI. Unlike traditional SEO that targets classic SERPs, LLMO addresses AI-generated responses. LLMO strategies include creating authoritative content with clear sources, using structured data, building a strong online brand presence, and producing content that directly answers user questions. LLMO is closely related to GEO (Generative Engine Optimization) but focuses specifically on language models rather than generative search engines as a whole.
Key Points
- Content optimization for LLM-generated responses (ChatGPT, Gemini, Claude)
- Close to GEO but specifically targeting language models
- Priority on authority, structure, and content factuality
Practical Examples
Optimizing for LLM citations
Structure your content with clear definitions, bullet lists, and sourced factual data. LLMs favor well-structured, factual, and authoritative content for generating their responses.
Brand presence
Multiply your brand mentions on reliable sources (Wikipedia, press, authority sites) to increase the probability that LLMs will cite you in their responses.
Frequently Asked Questions
GEO is a broader term encompassing optimization for all generative engines (Google AI Overviews, Bing Copilot, etc.). LLMO focuses specifically on large language models like ChatGPT or Claude. In practice, the strategies largely overlap.
Use specialized tools like Otterly.ai or Peec AI to monitor your brand mentions in LLM responses. You can also manually test by asking questions related to your industry in ChatGPT, Gemini, or Perplexity.
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Last updated: 2026-02-07