LLM SEO.

LLM SEO is the emerging practice of optimising digital content and brand signals to improve visibility in the responses generated by large language models — including ChatGPT, Claude, Gemini, Perplexity, and similar AI systems. As consumers increasingly use conversational AI for discovery, recommendations, and research, appearing in LLM-generated responses is becoming a distinct and measurable brand objective.

How LLMs Select Sources

LLMs generate responses from training data (knowledge encoded during model training) and, increasingly, retrieved web content via RAG (Retrieval-Augmented Generation). Content included in LLM training data earns presence in non-retrieved answers; content crawlable by the LLM's retrieval index earns presence in grounded, cited answers. Both pathways require high-quality, factually accurate, entity-rich content.

LLM SEO Signals

Research and practitioner consensus on LLM citation signals includes: strong entity recognition (brand mentioned in Wikipedia, Wikidata, Crunchbase, and authoritative third-party sources), factually accurate and specific content (LLMs favour verifiable claims), clear Q&A structure (content that directly answers specific questions), structured data markup, high domain authority from genuine editorial links, and brand consistency across all digital touchpoints.

LLM SEO vs. Traditional SEO

Traditional SEO optimises for algorithm signals measurable in search engine crawl data. LLM SEO optimises for model training and retrieval signals that are less transparent and harder to measure. The two overlap significantly — content that ranks well in traditional organic search is generally well-positioned for LLM citation too, because both reward quality, authority, and accuracy. LLM SEO is an extension of, not a replacement for, traditional SEO.