GEO (Generative Engine Optimization) is the practice of optimizing content to surface in generative AI responses, including being cited as a source.
GEO and AEO are closely related and often used interchangeably. The narrower distinction sometimes drawn: GEO emphasizes the broader generative process (training data influence, retrieval-augmented generation, citation likelihood), while AEO emphasizes the user-facing answer surface specifically.
In practice, the techniques overlap substantially: structured content (TL;DR leads, H2 questions, tables, FAQ blocks), AEO surfaces (llms.txt, Markdown twins), authoritative signals (E-E-A-T), and earned mentions from sources AI engines trust (Reddit, Wikipedia, established publications).