Generative Engine Optimization (GEO)
Generative Engine Optimization, or GEO, is the practice of shaping content so that it is more likely to be retrieved, cited, and favorably summarized by generative AI systems, such as ChatGPT, Perplexity, Google Gemini, Claude, Copilot, etc, when they answer a user’s question. The term was formally introduced in a 2023 research paper by authors from Princeton, Georgia Tech, the Allen Institute for AI, and IIT Delhi, which proposed a benchmark and measured that tactics such as adding statistics, direct quotations, and citations to a source could increase its visibility inside generative answers by a large margin. GEO differs from traditional search engine optimization in its target and its metric: instead of optimizing for a ranking position in a list of blue links that a human will scan, GEO optimizes for being selected, quoted, or paraphrased as evidence inside a synthesized answer that the human may never click through from. Practical techniques include writing clear, self-contained passages that answer a specific question, structuring content with explicit facts and sources, maintaining crawlable and machine-readable pages, and publishing an llms.txt file to guide AI crawlers, much as robots.txt and sitemaps guide traditional search engines.