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The 2026 vocabulary of Generative Engine Optimization, with live per-term citation status across ChatGPT, Perplexity, Claude, and Copilot.
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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.
GPT·Plx·Cld·Cop0×Gem·advanced
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.
GPT·Plx·Cld·Cop·Gem·advanced
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.
GPT0×Plx·Cld0×Cop0×Gem·