Generative engine optimization (GEO)
Generative engine optimization (GEO) is the practice of rewriting web content to improve its visibility in LLM-generated answers, rather than in traditional search-engine rankings — the successor problem to SEO for an AI-chatbot-mediated web. The term and its original strategy taxonomy (Quotes, Statistics, Authoritative, Fluency, Simple Language, Technical Terms, Unique Words) come from Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan & Deshpande's "GEO: Generative Engine Optimization" (KDD 2024); it has since been extended to conversational search (Puerto, Gubri, Green, Oh & Yun, "C-SEO Bench," NeurIPS 2025) and, in this vault's first encounter with the term, to electoral visibility specifically — Zhang & Xiao's "VoteGEO-Bench" (arXiv:2610.21330, 2026) coins "VoteGEO" for GEO applied to candidate and party exposure in election-answer queries.
Entered the vault via this one benchmark paper; not yet load-bearing elsewhere. Watching rather than hubbing, since the term's standing in this vault rests on a single unrefereed preprint so far — promote to a full hub if a future capture reads the founding KDD 2024 paper, C-SEO Bench, or another GEO-adjacent source directly.