Generative engine optimization (GEO)
Definition
Generative engine optimization (GEO) is the practice of shaping content so AI answer engines, such as ChatGPT, Perplexity and Google AI Overviews, cite or mention it in their answers. It is the AI-search counterpart to SEO.
By Ronan Liedmeier, fractional head of growth and course author · Published 17 October 2026 · Last updated 17 October 2026
Also called: GEO, answer engine optimization (AEO), AI search optimization
Buyers now ask these tools for vendor shortlists and product comparisons, and a brand that is never cited is missing from that shortlist. The name comes from a 2023 paper by Pranjal Aggarwal and colleagues, which reported that GEO methods can raise a source's visibility in generative engine responses by up to 40%, with results varying by subject area.
How it differs from SEO
SEO aims for a high position in a list of links. GEO aims to be one of the few sources an engine quotes when it writes an answer, and a page can rank well in Google and still never be cited. Engines often break one question into several sub-questions and search each, so a site needs pages that answer those sub-questions too.
Engines can only cite what they can read. Pages that block AI crawlers or load their text with JavaScript can be invisible to them. Clear, self-contained answers and specific figures with sources raise the odds of being cited. Each engine draws on different sources, so results need checking engine by engine.
Example
A marketer at a payroll software company keeps a sheet of 20 questions buyers ask, runs them weekly in three AI engines, and logs which brands get named. Her company appears in none. She finds the site's bot settings block AI crawlers, fixes that, and adds a plain comparison page with a table.
Common mistake
Treating GEO as on-page tweaks alone. Much of what engines cite sits on other people's sites, so press coverage, reviews and community answers belong in the plan.
Learn it in AI Academy
- GEO / AEO: Being the Answer, Not the ResultExpert · 9 min
Related terms
- Retrieval-augmented generation (RAG)
- Retrieval-augmented generation (RAG) is a way of getting an AI model to answer from your own documents. A search step first finds the passages that match the question, then the model writes its answer using those passages.
- Deep research
- Deep research is a mode in AI assistants where the model runs many web searches on its own, reads the sources it finds and writes a longer report with citations. It takes longer than a normal answer and suits questions that need several sources compared.
- Large language model (LLM)
- A large language model (LLM) is an AI system trained on huge amounts of text to predict which words should come next. At scale, that one skill lets it answer questions, summarize, translate and write in a way that reads like a person wrote it.