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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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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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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Context assembly
Context assembly is the stage between retrieval and generation where an AI system selects, orders, and packs retrieved passages into the model's context window before it generates an answer. In the retrieve-then-generate pipeline (RAG, Lewis et al. 2020), retrieval finds candidate passages; assembly decides which of them actually enter the prompt, in what order, within the token budget. It is where position effects like lost-in-the-middle (Liu et al. 2023) bite, so it can affect whether a retrieved passage is used, not retrieval alone.
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Context rot
Context rot is the empirically observed degradation in an LLM's output quality as its input context grows longer, even on simple tasks and well below the model's maximum context window. Formalized by Chroma's 2025 study across 18 models, it is distinct from context-window overflow (hitting the hard token limit) and broader than lost in the middle (which is specifically positional): context rot is degradation along the length axis. For publishers it reinforces that a retrieval pipeline keeps only a bounded, high-signal context, so concise self-contained passages survive better than verbose padding.
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Retrievability
Retrievability is an information-retrieval measure (Azzopardi & Vinay 2008) of how easily a document can be retrieved across a whole population of queries: the more queries that return it, and the higher its rank, the more retrievable it is. In AI search it names the upstream lever that content optimization skips, whether the engine's retrieval step can find and pull your page into the answer at all, which the GEO evidence suggests may be a more durable lever than isolated in-page rewrites.
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Retrieval pipeline
The retrieval pipeline is the index-retrieve-rerank-assemble-generate chain between a page and its answer; you harden the passage, not the pipeline.
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