Large Language Models (LLMs) are neural-network–based systems trained on vast corpora of text to learn statistical patterns, relationships, and contextual representations of words and phrases. By converting text into high-dimensional embeddings and using transformer architectures, LLMs can generate human-like language, complete prompts, answer questions, and extract semantic meaning. In the context of AI SEO, LLMs serve as both the retrieval mechanism and answer generator: they scan embeddings to locate conceptually relevant passages, rank candidate snippets based on contextual fit, and then compose or cite those passages as part of an AI-driven response. Optimizing for LLMs means structuring content, entities, and schema data so that the model’s embedding space correctly “sees” your page as authoritative, semantically aligned, and citation-worthy for a given query.