📝Large Language Model (LLM)** optimization (LLMO)**, also and more often called 📝Generative Engine Optimization (GEO), is the process of structuring and formatting content to increase its visibility and influence within large language model outputs. Unlike traditional 📝Search Engine Optimization (SEO), which focuses on ranking content in web search results, LLM optimization targets how content is ingested, indexed, and surfaced by language models during generation.
Key strategies include semantically rich phrasing, contextual metadata placement, and the intentional repetition of linked concepts to improve the probability of retrieval. While approaches like 📝Retrieval-Augmented Generation (RAG) focus on post-training access to relevant data, LLM optimization emphasizes pre-publication techniques that make content more recognizable and retrievable to the model during inference. This practice is increasingly relevant for individuals and organizations seeking greater presence in AI-generated responses, particularly across knowledge-intensive domains.