[GTM glossary](/glossary.md) Generative engine optimization Contextual Intelligence & AI # Generative engine optimization (GEO) In short Generative engine optimization (GEO) is the practice of making your brand and content more likely to be retrieved, cited, and accurately summarized by generative AI tools such as chat assistants and AI search features. Where classic SEO aims for a ranked link, GEO aims for inclusion inside a generated answer. Buyers increasingly ask AI tools for vendor shortlists and comparisons, so what those tools say about you can shape consideration before anyone visits your site. ## GEO vs. neighboring terms GEO focuses on generative answers broadly. Answer engine optimization targets direct-answer experiences, and LLM optimization focuses on language model behavior. In practice the tactics overlap heavily, and teams often use the terms interchangeably. --- ## What tends to help * **Clear, factual pages:** Plain statements of what you do, for whom, and how, in wording a model can quote. * **Structured content:** Headings, definitions, comparison tables, and FAQ sections are easy to extract. * **Consistent facts everywhere:** Keep names, categories, and descriptions aligned across your site, profiles, and listings. * **Third-party mentions:** Reviews, analyst or community discussions, and partner pages give models independent sources. * **Fresh updates:** Keep pricing, features, and dates current. --- ## How to run a GEO program * **Build a prompt list:** Collect 30 to 50 questions buyers would ask an AI tool about your category. * **Record the answers:** Note whether you are mentioned, how you are described, and which sources are cited. * **Find gaps:** Look for wrong facts, missing mentions, or competitors cited where you are not. * **Fix the sources:** Update or create content that addresses the gaps, then re-test on a schedule. --- ## Example Say a prompt like "best tools for account research" never mentions your product, and the cited sources are two comparison articles. You would aim to be covered accurately in similar third-party pages and publish a clear category page of your own. --- ## Common mistakes * **Chasing guarantees:** Model outputs vary run to run; track trends, not single answers. * **Keyword stuffing:** Unnatural text rarely helps and can hurt trust. * **Ignoring accuracy:** A mention with wrong facts can do harm. --- ## Generative engine optimization FAQs **What is generative engine optimization (GEO)?** Generative engine optimization is the practice of improving how AI answer tools find, cite, and describe your brand. It involves clear factual content, consistent information across sources, and third-party mentions so generated answers represent you accurately. **How is GEO different from SEO?** SEO aims to rank pages in a list of search results. GEO aims to have your brand included and correctly described inside an AI-generated answer. They share foundations like quality content and authority, but GEO also depends on how models retrieve and summarize sources. **How do you measure generative engine optimization?** Run a fixed set of buyer-style prompts across AI tools on a schedule and record whether you are mentioned, how you are described, and which sources are cited. Track changes over time, since individual answers vary between runs. **Does GEO replace SEO for B2B companies?** No. Search engines still drive significant traffic, and many AI tools draw on web content that good SEO helps create. Most B2B teams treat GEO as an extension of SEO and content work rather than a replacement. Share term --- ## More related phrases * [SEO](/glossary/seo.md) * [AI for sales](/glossary/ai-for-sales.md) * [AI hallucination](/glossary/ai-hallucination.md) ## Similar phrases * [AI search visibility](/glossary/ai-search-visibility.md) AI search visibility is how often and how favorably your brand appears in answers from AI assistants and AI search features, whether as a mention, a recommendation, or a cited source. * [Answer engine optimization (AEO)](/glossary/answer-engine-optimization.md) Answer engine optimization (AEO) is the practice of structuring content so AI assistants and search features can extract it and use it as a direct answer to a question. * [LLM optimization](/glossary/llm-optimization.md) LLM optimization is the practice of improving how language models perform on a task, or how well your content is understood and cited by them, through prompts, context, evaluation, and structure. * [Large language model (LLM)](/glossary/large-language-model.md) A large language model (LLM) is an AI system trained on huge amounts of text to understand and generate language, powering tools such as chatbots, email drafting, and research assistants for GTM teams. * [Retrieval-augmented generation (RAG)](/glossary/retrieval-augmented-generation.md) Retrieval-augmented generation (RAG) is a technique where an AI model first looks up relevant documents or data, then uses them to write an answer grounded in that source material.