Generative Engine Optimization

Generative Engine Optimization (GEO) examines how changes to web content affect what AI search and recommendation systems retrieve, cite, and say about a source. Common approaches include controlled edits to content structure and query alignment, retrieval-augmented generation tests, and agentic systems that iteratively rewrite pages using engine feedback. Researchers also study how to measure visibility across prompts and repeated runs, and whether changes actually cause more traffic or citations. A central tension is that optimization can improve discoverability but also manipulate recommendations or weaken content quality, while results vary with retrieval, brand authority, and the engine being tested.