All terms
The 2026 vocabulary of Generative Engine Optimization, with live per-term citation status across ChatGPT, Perplexity, Claude, and Copilot.
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A
Cluster pillar
AI search evaluation
AI search evaluation measures how AI engines retrieve, ground, and cite sources: academic benchmarks, vendor evals, and practitioner probing compared.
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AIPREF (AI usage preferences)
AIPREF is the IETF AI Preferences working group's effort to standardize a machine-readable way for content owners to express how their content may be used by AI systems. The preference is carried by a Content-Usage signal, attached as an HTTP response header or a robots.txt rule, using a small vocabulary (currently the categories train-ai and search, each set to y or n). AIPREF declares a usage preference; it does not authenticate the requester (out of scope) and does not enforce compliance.
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B
advanced
Black-hat C-SEO
Black-hat C-SEO is the use of adversarial techniques (most notably prompt injection hidden in page content) to manipulate an AI engine's ranking or citation behavior through deception rather than genuine content quality. It is the adversarial counterpart to white-hat C-SEO, which improves a page's actual clarity and usefulness. Beyond likely violating many platform terms, black-hat C-SEO is detectable, unreliable as models and defenses evolve, and a poor bet given that even the white-hat methods tested in C-SEO Bench show limited measured effect.
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BM25
BM25 (Best Matching 25) is a probabilistic ranking function used by classical search engines and the lexical layer of modern hybrid retrieval systems. It is the standard mechanism for scoring exact-keyword match in search retrieval; its application inside specific commercial AI search engines is not vendor-documented but is consistent with observable lexical-signal behavior.
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C
advanced
C-SEO Bench
C-SEO Bench is the Puerto et al. 2025 NeurIPS Datasets & Benchmarks paper that evaluates 9 Conversational Search Engine Optimization methods across 6 domains, two tasks (question answering + product recommendation), and continuous multi-actor adoption rates. Its headline finding is that most current C-SEO methods are largely ineffective once tested outside the single-actor synthetic conditions of prior GEO benchmarks; a traditional retrieval-ranking SEO baseline (moving the source to context position 1) is roughly 7.6× more effective in their retail-domain measurement than the best C-SEO method tested.
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Citation precision and recall
Citation precision is the fraction of citations in an AI engine's response that actually support the sentence they are attached to. Citation recall is the fraction of generated sentences that are fully supported by their citations. Both are model-behavior metrics, not publisher-visibility metrics: they measure how faithfully an AI engine uses the sources it cites, not how often a publisher's content appears as a source.
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Cited-version Lag
Cited-version lag is when an AI answer engine cites a page's current URL but reproduces a claim that appeared in an earlier version of that page and has since been corrected. The observable part is a version mismatch; the behavior is consistent with a stale retrieved or indexed representation, though the exact stale layer is usually not identifiable from the answer interface. Distinct from citing genuinely old content and from a knowledge cutoff. Coined by GEO Glossary for this precise, under-named case, detectable only where the engine renders the exact fact that was corrected.
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