How to use this glossary
This glossary serves two audiences. Marketers learning the GEO and AEO field can read it top to bottom and walk away with the working vocabulary. Writers, analysts, and journalists covering AI search can pull a single one-sentence definition for direct use in an article, a deck, or a research note. Every entry is built so the opening line is short, factual, and quotable on its own.
Wherever a term has changed meaning recently, the entry calls out the year the term entered common usage and what shifted. Where a term overlaps with another, the cross-link sends you to the closest neighbor in the field.
Quick navigation
A
Answer Engine Optimization (AEO)
Answer Engine Optimization is the practice of structuring web content so that answer engines and AI assistants extract a clear, direct answer to a user question and surface that answer back, often with attribution.
AEO grew out of the rise of voice assistants and featured snippets in the late 2010s and matured as ChatGPT, Perplexity, and Google AI Overviews shifted search toward direct answers. AEO is narrower than GEO. It focuses on getting one specific question answered by your page, while GEO focuses on the broader pattern of being cited across many AI answers.
Example: A wealth advisor publishes a page titled "What is a Roth conversion ladder," opens with a 30-word definition, and adds FAQ schema. ChatGPT and Google AI Overviews lift that paragraph as the direct answer when users ask the question.
AI Citation Rate
AI Citation Rate is the percentage of tracked AI answers in a given prompt set that name your brand or link your domain as a source.
Citation Rate is one of the three core GEO performance metrics, alongside Brand Mention Frequency and Share of Voice. The metric became standardized in 2025 once tracking platforms like Peec.ai, Profound, and Otterly began running prompts on a recurring schedule and logging structured outputs.
Example: An M&A advisor tracks 50 buying-stage prompts each month. In April, the firm appears in 12 answers. The April citation rate is 24%.
AI Mode (Google)
AI Mode is a dedicated tab inside Google Search that runs queries through a Gemini-powered conversational interface and returns a generated answer with linked sources.
AI Mode launched in 2025 as the deeper-conversation surface that sits next to AI Overviews. Where Overviews appears as a block above the blue links on a normal results page, AI Mode is a full chat experience. Both surfaces draw on the same underlying retrieval and generation stack but optimize for different user intents.
Example: A buyer types "compare boutique M&A advisors for a $40M SaaS exit," switches to AI Mode, and receives a multi-paragraph comparison with three named firms cited inline.
AI Overviews (Google)
AI Overviews is the Gemini-generated answer block that appears at the top of select Google Search results pages and cites supporting source URLs.
Google introduced the experience as Search Generative Experience (SGE) in 2023 and rebranded it AI Overviews when it rolled out broadly in May 2024. The block draws on Google's index, gives the answer first, and links to a small set of sources. Coverage and trigger rate continue to expand quarter by quarter.
Example: A user searches "best STR property management software for owners with 10 plus units." AI Overviews returns a six-sentence answer with three vendor names and four cited URLs, then the standard blue links appear below.
AI Referral Traffic
AI Referral Traffic is the segment of website visits that arrive from AI search interfaces such as ChatGPT, Perplexity, Gemini, and Microsoft Copilot.
AI referral sessions started showing up as a meaningful traffic source in late 2023 and grew rapidly through 2025. The traffic tends to be lower volume than Google organic but higher intent because the model has already pre-qualified the user against the prompt.
Example: In Google Analytics 4, an STR management firm filters source by chatgpt.com, perplexity.ai, and gemini.google.com and sees 412 sessions in April with a contact-form conversion rate three times the site average.
AI Search Optimization
AI Search Optimization is the umbrella discipline of preparing a brand and its content to be found, cited, and recommended inside AI-generated search experiences.
The label functions as a parent category that covers GEO, AEO, and LLM SEO. Practitioners usually pick a more specific term when describing a tactic, but AI Search Optimization is the phrase most often used in executive-level briefings and budget discussions.
Example: A CMO presents the 2026 marketing budget with a new line item called AI Search Optimization, covering content restructuring, schema implementation, citation tracking, and inclusion outreach.
Answer-first Content
Answer-first Content is editorial structure that places the direct answer to a query in the opening lines of the page, before context, history, or supporting detail.
The format is the on-page workhorse of AEO. AI engines and featured-snippet systems both reward pages that put the answer at the top because extraction is cleaner and the chance of attribution rises. The pattern is sometimes called inverted pyramid in journalism.
Example: A page titled "What is GEO" opens with a 28-word definition, follows with a 60-word expansion, and only then moves into history, examples, and related concepts.
B
Brand Mention Frequency
Brand Mention Frequency is how often a brand name appears across a tracked set of AI answers over a fixed time window.
Mention frequency is the rawest visibility metric in GEO. It does not care whether the brand is recommended, listed, or only referenced in passing. The number is most useful as a trend line. A rising mention count signals that the brand is showing up more often, which usually precedes lifts in citation rate and share of voice.
Example: Across 60 tracked prompts each month, a financial advisor's brand name was mentioned 14 times in February, 22 times in March, and 31 times in April.
C
ChatGPT Search
ChatGPT Search is OpenAI's web-connected answer mode inside ChatGPT that retrieves live results and returns a synthesized answer with linked source citations.
OpenAI launched the surface in 2024, opening a new citation pathway separate from the static training-data answers ChatGPT had returned before. ChatGPT Search relies heavily on the Bing index, which means Bing Webmaster Tools indexing has become a high-impact GEO step.
Example: A user asks ChatGPT, "What are the best AEO agencies for B2B SaaS in 2026," and ChatGPT Search returns five named agencies with numbered citations linking to the source URLs.
Chunked Content
Chunked Content is editorial formatting that breaks pages into short, self-contained passages, each addressing a single question or concept that an AI engine can extract independently.
The technique mirrors how retrieval pipelines actually work. AI engines split a page into smaller passages, embed each one, and retrieve the chunk that best matches the user query. Pages that are written in clean chunks get retrieved more often and cited more often than pages written as long unbroken essays.
Example: A pillar guide on tax-loss harvesting uses 18 H3 sub-questions, each followed by a 100-word self-contained answer, instead of a single 4,000-word essay.
Citable Source Page
A Citable Source Page is a URL structured so that AI engines can cleanly extract a quote, a statistic, or a definition with clear attribution.
Citable source pages share a few traits. They name the author, date the publication, place the key claim near the top, and use schema to mark up the entity, the dataset, or the FAQ. They are the asset class GEO programs invest in because they earn citations long after they are first published.
Example: A "State of AI Search 2026" report includes a clearly dated headline, a named author, an executive summary with five quotable stats, and Dataset schema on the methodology section.
Citation Source (in LLM context)
A Citation Source is a third-party URL that an AI engine references in its generated answer, either as a linked footnote or an inline attribution.
Different AI engines surface citations in different ways. Perplexity uses numbered footnotes. ChatGPT Search displays a side panel of source cards. Google AI Overviews shows a small set of linked source thumbnails. The shared idea is that the model is grounding its answer in identifiable third-party content.
Example: A Perplexity answer about retirement planning cites four sources, two of which point to articles on a specific advisor's domain. Both citations send qualified referral traffic.
Citation-to-Conversion Funnel
The Citation-to-Conversion Funnel is the path from a brand being cited inside an AI answer to a click, a session, and a measurable business outcome.
The funnel mirrors the classic awareness-to-conversion model but starts inside the model's response. Each step has its own metric: citation rate at the top, click-through rate from the AI surface, engagement rate on landing, and conversion rate to lead or revenue.
Example: A capital-raising firm tracks 40 prompts. April produced 96 citations, 14 click-throughs, 10 engaged sessions, and two booked discovery calls. The full funnel converts at 2.1%.
Claude (Anthropic)
Claude is the family of large language models built by Anthropic, used inside the Claude.ai assistant and as a reasoning engine for enterprise and developer tools.
Claude has become a popular choice for enterprise reasoning, long-context analysis, and content generation. Claude.ai itself does not provide live web grounding the way ChatGPT Search and Perplexity do, but Anthropic ships Claude inside platforms that often layer retrieval on top, which affects which sources Claude cites in those wrapped contexts.
Example: A consulting team uses Claude inside their internal research tool. The tool retrieves articles from a curated set of expert publications, which means brands featured in those publications get surfaced in Claude-generated briefs.
D
Dataset Schema
Dataset Schema is structured data markup that describes a published dataset and helps AI engines pull statistics with the original source attached.
Schema.org Dataset markup includes properties like name, description, creator, license, and distribution URL. AI engines use these signals to confirm a stat is grounded in a real dataset. Marking up original studies, surveys, and benchmark reports increases the likelihood that the numbers travel with attribution back to the publishing brand.
Example: A "State of GEO 2026" study includes Dataset schema covering the methodology, sample size, and license. When AI engines cite a stat from the study, they consistently link the source URL.
Decision-stage AI Query
A Decision-stage AI Query is a prompt where the user is comparing a small set of named brands or services and is close to a buying decision.
Decision-stage queries usually contain comparison language ("vs," "alternatives to," "best") plus a qualifier (segment, size, geography, budget). They are the most valuable prompts to track because the user is already pre-qualified, and the brand named first or named most often often wins the click.
Example: "Best M&A advisor for a SaaS company under $50M revenue with strategic acquirer interest" is a classic decision-stage prompt. The buyer is comparing options, has narrowed the segment, and is asking the model to recommend.
E
E-E-A-T (and the LLM equivalent)
E-E-A-T is Google's quality framework covering Experience, Expertise, Authoritativeness, and Trustworthiness, and is the closest analog to how AI engines weight which sources to cite.
Google added the second E for Experience in 2022. AI engines do not implement E-E-A-T the way Google's quality raters do, but the same signals (named authors with verifiable expertise, dated content, original research, third-party reviews, consistent brand mentions) line up with what models prioritize when selecting citation sources.
Example: Two pages on the same topic compete for citations. The one with a named author, a bio link, original data, and three dozen unprompted brand mentions across listicles wins almost every AI citation.
Engagement Rate (AI-referred)
Engagement Rate (AI-referred) is the share of AI-referred sessions that include a meaningful page interaction such as a scroll, a form view, or a multi-page visit.
AI referral traffic tends to engage at higher rates than typical social or display traffic, because the model has already screened the user. Tracking engagement separately from referrer source helps isolate which AI engines drive the highest-quality visits and where to invest more seeding effort.
Example: A wealth manager sees a 71% engagement rate from Perplexity referrals versus 38% from social. The team doubles down on Perplexity-specific seeding for the next quarter.
Entity Disambiguation
Entity Disambiguation is the process by which an AI engine decides which specific entity a name refers to when several share the same label.
When a brand name is generic, the model can confuse it with a person, a different company, or a place. Disambiguation relies on signals like context phrases ("the M&A advisor," "based in Dallas"), schema (sameAs links to LinkedIn, Wikipedia, Crunchbase), and consistent third-party mentions. Brands that lose at disambiguation lose citations to lookalikes.
Example: A boutique advisor named "Apex" adds sameAs links to its LinkedIn and Crunchbase pages, plus consistent "M&A advisor" context across press, and starts winning the disambiguation battle against three other "Apex" brands in unrelated categories.
Entity Recognition (NER in LLM context)
Entity Recognition is the process of identifying named entities like brands, people, and places inside content, so an AI engine can connect the entity to its profile in a Knowledge Graph.
Named Entity Recognition started as an NLP task in the 1990s. Inside the LLM era, the function is mostly invisible. Models still need to know which token strings are entities and which are common nouns. Schema, consistent capitalization, and structured author bios all feed cleaner entity recognition.
Example: A page about retirement planning consistently writes "Tania Kozar, ProCloser.ai" with a linked author bio. The repeated entity pattern helps AI engines tie quotes from Tania to the ProCloser.ai brand.
F
Fan-out Queries
Fan-out Queries are the multiple sub-questions an AI engine generates from a single user prompt to retrieve a broader set of sources before composing the final answer.
Google described the technique publicly in 2024 when explaining how AI Overviews picks its sources. A user asks a question, the system generates several internal variants, runs each through retrieval, then synthesizes one answer from the combined results. Brands that want to be cited need to show up across the fan-out variants, not just the original prompt.
Example: A user asks "best STR management software for 2026." The fan-out includes "top short-term rental property managers," "vacation rental management software for owners," and "Airbnb management software." A brand seeded across all three variants is far more likely to land in the AI Overview.
FAQ Schema
FAQ Schema is structured data markup that flags a list of question-and-answer pairs on a page so that search and AI engines can surface them as direct answers.
FAQ schema is the most common AEO format. Pairs of question and answer marked up with FAQPage schema have a long history of being lifted into featured snippets and AI Overviews. The trick is keeping each answer under 60 words and writing the question the way a real user would type it.
Example: A glossary page (this one) marks up its bottom FAQ section with FAQPage schema, helping AI engines confirm the question-and-answer structure and quote the answers verbatim.
Featured Snippet (vs AI Overview)
A Featured Snippet is a single excerpt Google pulls from one ranking page to answer a query, while an AI Overview is a multi-source synthesized answer generated by Gemini.
Featured Snippets, sometimes called position zero, have been part of Google Search since 2014. They are extractive (one source, lifted verbatim). AI Overviews are generative (multiple sources, synthesized by an LLM, citations attached). The two often appear together, with AI Overviews on top and a Featured Snippet below.
Example: A search for "what is GEO" returns an AI Overview with three citations and a Featured Snippet pulled from a single SEO blog. The same query, two formats, two different optimization plays.
G
Gemini (Google)
Gemini is Google's family of multimodal large language models and the engine that powers Google's AI Overviews, AI Mode, and the Gemini consumer assistant.
Google rebranded Bard as Gemini in early 2024 and unified its AI surfaces under the Gemini name through 2024 and 2025. Gemini taps Google's index for retrieval, which means classic Google ranking signals still influence which sources Gemini pulls in. Gemini is also embedded inside Google Workspace, where it draws from a different content surface.
Example: A user inside Google Docs asks Gemini for a quick summary of "best CPA firms for SaaS founders." Gemini surfaces three firms it has seen consistently cited in indexed listicles.
Generative Engine Optimization (GEO)
Generative Engine Optimization (GEO) is the practice of structuring content so AI engines like ChatGPT and Perplexity will cite it as a source in generated answers.
The term emerged in early 2024 as ChatGPT search adoption grew and academic researchers began publishing on how to influence which sources LLMs cite. GEO is to LLMs what SEO was to search engines. The same on-site signals (clear answers, structured data, dated content) plus off-site signals (brand mentions, listicle inclusion, expert quotes) work together to earn citations.
Example: A retirement-planning firm rewrites its top 30 service pages with answer-first openings, adds FAQ schema, publishes one original benchmark report, and lands in 18 of 50 tracked AI answers within two quarters.
H
High-intent AI Visitor
A High-intent AI Visitor is a user who arrives at your site after an AI engine has already filtered them to your brand based on a specific buying-stage prompt.
Compared to organic search visitors, AI-referred visitors usually skip awareness-stage research. They arrive having already read a synthesized answer that named your brand. That filter pushes engagement and conversion rates well above site averages, especially for considered B2B services.
Example: A wealth manager sees AI-referred visitors convert to discovery calls at 6.4% versus a 1.1% site-wide average. The visitor pool is smaller, but the lead quality is meaningfully higher.
K
Keyword Targeting (vs topic targeting)
Keyword Targeting is the older SEO practice of optimizing a page for a specific exact phrase, while topic targeting optimizes a page for the full set of questions and entities around a subject.
Keyword targeting was the dominant playbook from the mid-2000s until Google's BERT and MUM updates pushed semantic understanding forward. AI engines work at the topic level by default. Pages that cover the entities, sub-questions, and definitions inside a topic get cited more often than pages tightly tuned to a single keyword phrase.
Example: A page about "wealth management" that covers planning, tax, estate, and risk subtopics, with internal links to each, is cited by AI engines far more often than a page narrowly tuned to the phrase "wealth management firm."
Knowledge Graph
A Knowledge Graph is a structured database of entities and the relationships between them that AI engines and search engines use to ground answers in verified facts.
Google's Knowledge Graph launched in 2012 and was the first widely visible example. Microsoft, Meta, and major AI vendors run their own equivalents. Earning an entry in a Knowledge Graph (through Wikipedia, Wikidata, or major data partners) is one of the highest-impact off-site moves for entity-level AI visibility.
Example: A boutique M&A firm gets a Wikidata entry, links its sameAs from the firm's homepage schema, and starts appearing as a recognized entity in answers across multiple AI engines.
L
Linkable Asset
A Linkable Asset is a single page or resource designed to attract organic links and citations from other publishers, often a glossary, statistic library, calculator, or original study.
The category predates GEO. Backlinko's SEO glossary, for example, has earned more than 1,800 referring domains because writers cite it whenever they need a definition. The same logic applies in GEO. Linkable assets become the easiest URLs for AI engines to cite because they are quoted across the web with consistent attribution.
Example: The page you are reading is built as a linkable asset. Each definition is quotable in one line, the schema tags every term, and the citation block at the bottom gives writers an APA snippet ready to paste.
Listicle Format Bias
Listicle Format Bias is the observed tendency of AI engines, especially ChatGPT, to draw the majority of their citations from list-format articles such as best-of and top-ten pages.
Ahrefs published research in 2025 showing that 43.8% of all ChatGPT citations come from listicle-format pages. The format aligns with how LLMs prefer to extract content. Each list entry is a clean chunk with a name and a description. The model can pull one or several entries without losing context.
Example: A category leader audits the listicles ranking for its top 30 prompts and runs an inclusion outreach campaign. Two quarters later, the firm appears in 19 of those listicles and citation rate roughly doubles.
LLM-Native Buyer Journey
The LLM-Native Buyer Journey is a path to purchase that runs through one or more AI assistants for problem framing, vendor discovery, comparison, and shortlist creation before the buyer ever visits a brand site.
Buyers are increasingly finishing the early stages of research inside ChatGPT, Perplexity, or Gemini. By the time they click through to a brand site, they have already been pre-qualified by the model. Brands that want to participate in this journey have to invest in being cited at the discovery and comparison stages, not just the click stage.
Example: A founder uses ChatGPT to compare boutique M&A advisors, asks Perplexity to validate the shortlist, and only then visits two firm websites. The advisor named consistently across both engines wins the meeting.
LLM Seeding
LLM Seeding is the practice of placing a brand inside the third-party sources that large language models read, so the model is more likely to mention the brand in generated answers.
Seeding takes citation acquisition off the brand's own domain and out into listicles, review sites, expert roundups, Reddit threads, and podcast transcripts. The discipline borrows mechanics from PR and digital outreach but measures success in mentions and citations rather than backlinks. ProCloser.ai covers the full process in our LLM seeding strategy guide.
Example: A SaaS M&A advisor runs a quarter of inclusion outreach to land in 12 "best of" listicles. ChatGPT citation rate climbs from 8% to 31% over the next two months.
LLM SEO
LLM SEO is the practice of optimizing a website's structure, content, and signals so that large language models can index, understand, and cite the site inside generated answers.
LLM SEO sits inside GEO but focuses on the on-site half of the work. Crawler access for OAI-SearchBot, GPTBot, and PerplexityBot, clean semantic HTML, schema, llms.txt, dated content, named authors. These signals make the site machine-readable, which is the precondition for being cited.
Example: A wealth manager confirms in robots.txt that GPTBot, OAI-SearchBot, and PerplexityBot are allowed, ships clean HTML on every service page, and sees ChatGPT Search citations rise within the next refresh cycle.
llms.txt
llms.txt is a proposed plain-text file at the root of a domain that lists which pages a site recommends to large language models, written for AI consumption rather than human readers.
The proposal originated in 2024 as a way for site owners to point AI agents at their highest-value reference pages. The file uses Markdown, names the most important sections, and links to canonical URLs. Adoption is still uneven across AI engines, but the cost of shipping the file is low and the upside is meaningful for content-heavy publishers.
Example: A publisher places /llms.txt at the root of its domain, listing the glossary, top guides, and original research. Tools that respect the file send agents to those URLs first.
M
Microsoft Copilot
Microsoft Copilot is the family of AI assistants built into Bing, Windows, Edge, and Microsoft 365 that uses GPT-class models with the Bing index for web-grounded answers.
Copilot reaches users through Windows, Edge, the Bing chat surface, and the embedded assistant inside Microsoft 365. Because Copilot grounds on Bing's index, Bing Webmaster Tools indexing is a prerequisite for visibility. The same pages that earn ChatGPT Search citations often earn Copilot citations because both surfaces rely on Bing.
Example: A B2B brand confirms its key pages are indexed in Bing Webmaster Tools, then sees both ChatGPT Search and Copilot pick the brand up in answers within two indexing cycles.
P
PageRank (and why it matters less for AI)
PageRank is Google's original algorithm for scoring the importance of a web page based on the structure of links pointing to it, and it carries less weight inside AI answers than brand-mention signals do.
PageRank still influences classical Google ranking, which means it still influences which pages Gemini retrieves for AI Overviews. But pure link counts matter much less inside ChatGPT and Perplexity. Brand mentions, listicle inclusion, and consistent third-party context outperform link equity for citation outcomes.
Example: A brand with a high-authority backlink profile but few unprompted brand mentions in listicles loses every ChatGPT comparison query to a smaller brand that gets named consistently across the listicle layer.
Passage-level Optimization
Passage-level Optimization is the discipline of writing each paragraph as a self-contained answer so an AI engine can lift one passage as a citation without needing the surrounding article.
Google announced passage indexing in 2020. AI engines extended the same logic. Retrieval pipelines split pages into passages, embed them, and pull the best-matching passage for a query. Pages built around clean, self-contained passages get retrieved more often and quoted more often than pages where the answer is spread across several paragraphs.
Example: A 3,000-word guide on AEO is restructured into 22 H3 sub-questions, each followed by a 70-to-110-word self-contained answer. AI Overviews cites the page across five different sub-question queries.
Perplexity
Perplexity is an AI search engine that retrieves live web results and returns a synthesized answer with numbered, linked source citations alongside follow-up suggestions.
Perplexity launched in 2022 and grew quickly with research-oriented and analyst-style users. Its citation surface is the most explicit of the major AI engines, with numbered sources and a side panel of source cards. Perplexity weights recency heavily, which makes recently dated content disproportionately valuable.
Example: A user asks Perplexity, "What is the difference between GEO and AEO?" The answer cites four sources, two of which are recently published guides. Both publishers see a clear lift in referral sessions over the following week.
R
Review Schema
Review Schema is structured data markup that describes a review or rating, helping search and AI engines surface star ratings, reviewer identity, and review counts inside answers.
Schema.org Review and AggregateRating let publishers expose first-party review data in a machine-readable form. AI engines weight reviews when generating recommendation answers because review signals look like trust evidence. Real reviews from real users marked up correctly are a meaningful boost for any service brand competing in comparison answers.
Example: A retirement-planning firm marks up its 87 verified Google reviews with AggregateRating schema and starts being named as a "highly reviewed" option in AI Overviews comparison answers.
S
Search Engine Optimization (SEO)
Search Engine Optimization (SEO) is the discipline of improving a website's organic visibility inside traditional blue-link search engine results pages such as Google and Bing.
SEO has been the dominant organic-acquisition discipline since the late 1990s. The core mechanics (crawlability, on-page relevance, off-site authority, user signals) still matter for AI search, but the success metric is shifting from rankings to citations. SEO and GEO overlap heavily on inputs and diverge on outcomes.
Example: A B2B services brand keeps its SEO program running for blue-link traffic and adds a parallel GEO program tracking AI citation rate, share of voice, and AI referral traffic. The two report side by side in the monthly executive review.
Semantic Density
Semantic Density is the concentration of relevant entities, definitions, and relationships per unit of text, a key signal AI engines use to judge whether a passage is worth citing.
High semantic density does not mean cramming keywords. It means each sentence carries a meaningful entity or relationship. Pages with low density (filler, throat clearing, repetition) underperform in retrieval. Pages with high density read tightly and earn more citations.
Example: A glossary entry that names the term, dates the origin, lists two related concepts, and gives a concrete example in 100 words has higher semantic density than a 400-word essay that buries the same facts in narrative.
Share of Voice (in AI answers)
Share of Voice (in AI answers) is your brand's mentions divided by the total brand mentions across a tracked AI answer set, expressed as a percentage.
Share of Voice is the competitive citation metric. Where Citation Rate measures presence, Share of Voice measures dominance. A 15% share against five named players signals you are roughly even. A 40% share signals the model treats you as the default answer.
Example: Across 60 tracked prompts, your brand was named 31 times against 100 total mentions of all named brands. Share of Voice for the month is 31%.
Structured Data for LLMs
Structured Data for LLMs is the use of Schema.org markup, JSON-LD, and clean semantic HTML to give AI engines unambiguous facts about a page, an entity, or a dataset.
Structured data is one of the highest-impact on-site GEO signals because it removes ambiguity. AI engines do not have to guess whether a string is an author name, a publication date, or a company name. Article, FAQPage, DefinedTermSet, Dataset, Review, and BreadcrumbList are the workhorses.
Example: This glossary uses BlogPosting, DefinedTermSet, BreadcrumbList, FAQPage, and WebPage schema in a single @graph, giving AI engines machine-readable context for every term.
T
Topical Authority (LLM-relevant)
Topical Authority is the depth and breadth of a site's coverage on a single subject, measured by AI engines through entity coverage, internal linking, and recurring third-party brand mentions in that topic.
Topical authority predates AI search but matters more inside it. AI engines cite a small set of trusted sources per topic. Sites that cover a topic deeply, with named authors and consistent third-party mentions in that exact space, earn the citations that thinner sites cannot reach.
Example: A boutique M&A firm publishes 40 connected pages covering exit planning, valuation, deal structuring, and post-close transition. Within two quarters, the firm becomes one of three brands AI engines name on most M&A advisory prompts.
Trust Signal (LLM-specific)
An LLM Trust Signal is any feature an AI engine uses to decide whether a source is safe to cite, including author bios, dates, named expertise, third-party reviews, and consistent brand mentions.
AI engines do not implement trust the way humans do. They lean on consistent, machine-readable signals. A page that names a verifiable author, dates itself, links its sources, and is mentioned consistently across third-party listicles is treated as more trustworthy than a comparable anonymous page with no third-party context.
Example: Two equally well-written wealth-planning articles compete for citations. The one with a named author bio, a recent date, and 30 unprompted listicle mentions wins almost every relevant AI answer.
TrustRank (ProCloser methodology)
TrustRank is ProCloser.ai's five-step methodology for building brand citations across ChatGPT, Perplexity, Gemini, and AI Overviews through prompt mapping, baseline auditing, listicle targeting, inclusion outreach, and share-of-voice tracking.
We named the system before "LLM seeding" became a common label. The five steps are unchanged: map the prompts that drive your buyers, audit current citations, target the listicles your category already runs through, run inclusion outreach, and track share of voice across each model. More detail at our methodology page.
Example: A ProCloser.ai client in M&A advisory ran the full TrustRank loop and lifted ChatGPT share of voice from 6% to 38% across 50 buying-stage prompts in two quarters.
How to cite this glossary
Use the snippets below when quoting any definition from this page in an article, slide, or research note. The page is a stable URL with a dated last update, which makes attribution straightforward.
Kozar, T. (2026). The 2026 glossary of GEO, AEO, and AI search optimization terms. ProCloser.ai. https://procloser.ai/blog/geo-aeo-glossary/
@misc{kozar2026geoaeoglossary,
author = {Kozar, Tania},
title = {The 2026 Glossary of GEO, AEO, and AI Search Optimization Terms},
year = {2026},
url = {https://procloser.ai/blog/geo-aeo-glossary/},
note = {ProCloser.ai}
}
<blockquote> Generative Engine Optimization (GEO) is the practice of structuring content so AI engines like ChatGPT and Perplexity will cite it as a source in generated answers. <cite>Kozar, T. (2026). <a href="https://procloser.ai/blog/geo-aeo-glossary/">The 2026 Glossary of GEO, AEO, and AI Search Optimization Terms</a>. ProCloser.ai.</cite> </blockquote>
About this glossary
This glossary is maintained by ProCloser.ai, a GEO and AEO agency working with M&A advisors, financial services firms, and professional services brands. Entries are curated by Tania Kozar with input from the ProCloser research team. Definitions are reviewed quarterly so that fast-moving terms (AI Mode, llms.txt, fan-out queries) stay current.
Frequently asked questions
What is the difference between SEO, GEO, and AEO?
SEO targets blue-link rankings on Google and Bing. GEO targets brand citations inside generated answers from ChatGPT, Perplexity, Gemini, and AI Overviews. AEO is narrower than GEO and focuses on getting your page surfaced as the direct answer to a single question. The three disciplines overlap, but the success metric is different for each.
Which AI engines should I optimize for in 2026?
Optimize for ChatGPT and Google AI Overviews first because they carry the largest reach. Add Perplexity for research and analyst-grade buyers, Microsoft Copilot for Windows and Edge users, and Gemini for Workspace accounts. Claude is rising for enterprise reasoning. Cover the four web-grounded engines first, then add the rest.
Is GEO the same as LLM SEO?
GEO and LLM SEO overlap, but they are not identical. GEO is the broader strategic goal of being cited inside generated answers, on-site and off-site combined. LLM SEO is narrower and focuses on the on-site work, such as content structure, schema, llms.txt, and crawler access, that helps a single domain get indexed and cited by language models.
How fast is the GEO terminology evolving?
Faster than any SEO terminology cycle in the past decade. New terms like fan-out queries, llms.txt, and LLM seeding entered common usage between 2024 and 2026. Existing SEO terms like featured snippets and PageRank are being redefined in an AI context. Expect this glossary to shift several entries per quarter and to keep doing so through 2027.
Where can I learn more about GEO methodology?
Start with our companion guides on Generative Engine Optimization, Answer Engine Optimization, and LLM Seeding. For our internal methodology, see the TrustRank framework on the ProCloser methodology page. For external research, the Ahrefs 2025 ChatGPT citation study and ongoing analysis from Semrush and Position Digital are useful reference points.
Want your brand cited every time a buyer asks AI?
ProCloser.ai runs TrustRank, the LLM seeding system built for M&A advisors, financial services firms, and professional services brands. Book a free GEO audit to see your current AI citation baseline and where the gaps are.
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