Learn how large language models understand, describe, and recommend brands — and why modern SEO needs to account for AI-generated answers.
LLM Optimization (also called GEO — Generative Engine Optimization) is the practice of improving how large language models like ChatGPT, Gemini, Claude, and Perplexity understand, describe, and recommend your brand, products, and services.
Unlike traditional SEO, which focuses on ranking signals like backlinks, keywords, and technical health, LLM Optimization focuses on the quality and clarity of the signals AI systems use when generating answers about topics related to your business.
Traditional SEO optimizes for search engine ranking algorithms. LLM Optimization optimizes for language model understanding and citation decisions. When a user asks ChatGPT for the best IT company in Los Angeles, the model does not crawl Google. It draws on training data, cited sources, entity knowledge, and web context to generate an answer.
If your brand is not well-represented across those signals — your website, your citations, your reviews, your structured content, your FAQ answers — you are less likely to be mentioned.
AI models understand entities. Your business is an entity with attributes: name, location, services, expertise, industry, reviews, affiliations, and reputation. The clearer those entity signals are across your digital presence, the more likely AI systems are to understand and mention you accurately.
SerpHive's LLM Optimization Suite tracks prompt visibility, AI citations, brand memory audits, competitor AI mentions, entity clarity scores, and AI answer gaps — all from one dashboard.
SerpHive Team
SEO, AI visibility, local SEO & LLM optimization insights from the SerpHive platform team.
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