GEO

What is generative engine optimization

Generative engine optimization (GEO) is the practice of shaping content so that generative engines, AI systems that retrieve sources and then write an answer from them, use it and cite it. The term comes from a 2023 research paper, GEO: Generative Engine Optimization. Its authors defined ways to measure how visible a source is inside a generated answer, built a benchmark of queries to test against, and tried content edits such as citing sources, adding quotations and adding statistics.

In their tests those edits made sources more visible, by up to 40 percent on the paper's own measures, while keyword stuffing, a classic search tactic, did little. The useful lesson is the direction rather than the number: engines that summarise sources appear to favour passages with evidence attached.

Two cautions apply. Most of the experiments ran on a generative engine the researchers assembled for the study, with a smaller check on Perplexity, and production engines change how they retrieve and rank sources often. And visibility is not accuracy: an engine can cite a page and still misstate it.

Today GEO and AEO describe the same work, and this publication files articles under both. For tools that help revise pages, see content optimization in the directory; for tests you can repeat, see Experiments.