Learning hub / glossary
Plain-English definitions for the vocabulary of AI search — GEO, answer engines, citation engineering — written for the people who run technical B2B and industrial e-commerce, with an example from the catalog every time.
The glossary
AI Overviews are Google's AI-generated answer summaries shown above traditional results for many queries, citing a handful of source links. As of 2026 they appear on roughly 20-50% of searches (estimates vary widely by tracker and query mix), and most cited URLs are no longer drawn from the organic top 10.
AI search optimization is the umbrella discipline of making a brand discoverable, trusted, and citable across AI-powered search surfaces (Google AI Overviews and AI Mode, ChatGPT, Perplexity, Gemini). It spans GEO and AEO and extends into entity governance, structured data, and AI-crawler access. Also called AISO, AIO, or LLMO; terminology remains unsettled.
An answer engine is a search interface that returns a single synthesized answer instead of a ranked list of links, usually citing a handful of sources it drew from. The term predates generative AI (Ask Jeeves was described as one in the late 1990s, and WolframAlpha later) but now mostly means LLM-based tools like ChatGPT, Perplexity, and Google AI Overviews.
AEO is the practice of formatting content as direct, extractable answers (concise definitions, question-led sections, comparison tables) so answer engines like AI Overviews, Perplexity, and ChatGPT can lift a complete response and, ideally, cite your site. Attribution varies by engine and isn't guaranteed.
Citation engineering is the practice of deliberately structuring content, entities, and corroborating evidence so AI answer engines (ChatGPT, Perplexity, Google AI Overviews, Gemini) name your domain as a cited source. A citation-focused slice of GEO/AEO, it targets attribution in generated answers as the outcome — not blue-link rank, and distinct from local-SEO citation building (NAP business listings).
Generative engine optimization (GEO), coined in a 2023 Princeton/Georgia Tech paper (arXiv 2311.09735), is structuring a brand's content and data so AI answer engines (ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini) retrieve, summarize, and cite it in generated answers, rather than only ranking in a list of links. The term overlaps with AEO and "AI SEO."
LLM SEO is optimizing content so the large language models behind ChatGPT, Gemini, Perplexity, Copilot, and Claude surface and cite a brand in their answers. It overlaps heavily with GEO, AEO, and LLMO and is largely a catch-all synonym for AI search optimization rather than a distinct discipline.
An LLM citation is the source reference an AI answer engine (ChatGPT, Perplexity, Google AI Overviews) attaches to a generated answer, naming a page it claims to have drawn from. Earning citations differs from ranking: across AI assistants, most cited URLs don't rank in Google's top 10, though Google's AI Overviews still favor top-ranked pages more heavily.
Query fan-out is the technique Google's AI Mode (and similar AI search) uses to answer one prompt by silently issuing several related sub-queries, gathering passages for each, then synthesizing them — so pages that cleanly answer a sub-question can get pulled into the final answer even if they don't rank highly for the original query.
AI share of voice is your brand's share of mentions or citations in AI-generated answers (ChatGPT, Perplexity, Gemini, Copilot, Google AI Overviews/AI Mode) versus competitors, across a fixed set of buyer prompts. It is usually expressed as a percentage of total brand mentions, not of prompts answered, and is vendor-defined with no standard formula.
AI visibility is how prominently a brand shows up — as a mention or cited source — in the answers AI engines (ChatGPT, Gemini, Perplexity, Google AI Overviews) generate for its buyers' questions. The AI-search counterpart to search visibility, usually measured as mention rate, citation share, or share of voice rather than an ordinal rank.
An AI crawler is a bot that fetches web pages to feed AI systems — for training (GPTBot, ClaudeBot) or live answer retrieval (OAI-SearchBot, PerplexityBot). Blocking retrieval bots via robots.txt or bot protection removes content from AI search citations; blocking training bots only keeps it out of future models.
Grounding is tying an AI model's generated output to specific external sources — retrieved, looked up, or supplied at query time — so the answer reflects, and can cite, real documents and data instead of unverified model memory. Retrieval-augmented generation (RAG) is the leading grounding method, but not the only one.
RAG (retrieval-augmented generation) is the technique where an AI model fetches relevant external documents at query time and grounds its answer in them, instead of relying only on training data. It is why fresh, crawlable web content can be cited by AI search without being in a model's training set.
llms.txt is a proposed Markdown file at a site's root (e.g. /llms.txt), introduced by Answer.AI in 2024, listing a site's key pages in an LLM-friendly form. No major AI provider has committed to it; Google explicitly declined, though Perplexity reportedly uses it. Studies show no measured citation lift. Treat as low-cost, low-certainty hygiene.
A PIM (product information management system) is the central store for a catalog's product attributes, descriptions, and specs. In AI search it matters: incomplete or inconsistent PIM data propagates downstream as missing spec tables, broken or empty facets, and hallucinated AI answers about your products.
Part-number SEO is making every SKU discoverable by its manufacturer part number (MPN), OEM and competitor cross-references, and specs — in both traditional search and AI answers — so queries like "replacement for Parker 387 hose" resolve to your catalog page.
Part-number cross-reference content maps one manufacturer's part to its equivalents (OEM and competitor) in organized, text-based tables published as crawlable HTML — not locked inside a JavaScript lookup widget. Because each row answers a real "what replaces X?" query, it is among the most AI-citable assets a distributor can own.
A citation engineer is an emerging practitioner role focused on getting a brand cited in AI answers (ChatGPT, Perplexity, Google AI Overviews) — structuring extractable content, building entity consistency, and tracking citations. Distinct from local-SEO "citation building" (NAP directory listings). Today it is typically bought as a service outcome rather than filled as a standardized job title.
A GEO (generative engine optimization) specialist makes a brand visible inside AI answer engines such as ChatGPT, Perplexity, Gemini, Claude and Google's AI Overviews — implementing structured data, building entity and corroboration signals, controlling AI-crawler access (GPTBot, PerplexityBot, ClaudeBot, Google-Extended), and prompt-testing how often the brand is cited. Roughly synonymous with "AEO specialist."
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