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GEO & AI Search Glossary

Clear definitions of every key term in generative engine optimization, LLM SEO, and AI visibility — written for SEO professionals navigating the shift to AI-powered search.

A

AI Answer Engine
An AI answer engine is a search interface that synthesizes a direct, conversational answer to a user query rather than returning a ranked list of blue links. Examples include ChatGPT Search, Perplexity, Google AI Overviews, and Microsoft Copilot. Answer engines draw on a combination of training data, real-time retrieval, and language model reasoning to produce their responses. For SEO professionals, answer engines represent a new distribution channel: a site that is cited as a source gains visibility equivalent to, or greater than, a traditional top-10 ranking.
AI Overviews
AI Overviews is Google's name for the generative AI summaries that appear at the top of some search results pages, above the traditional organic listings. Launched broadly in 2024, AI Overviews are generated by Google's Gemini models and pull supporting citations from indexed web content. A citation in an AI Overview provides significant brand exposure because it appears before any organic result. Optimizing for AI Overviews requires clear, authoritative, structured content that directly answers likely query intents.Read the full guide →
AI Prominence Score
An AI prominence score is a metric that quantifies not just whether a domain was cited by an AI model, but how prominently that citation appeared in the response. A domain mentioned first, with a longer excerpt, or described as the primary source scores higher than one mentioned incidentally at the end of a list. Bingly calculates an AI prominence score per model per keyword to help teams prioritize optimization efforts based on quality of visibility, not just binary presence.
AI Visibility
AI visibility refers to the degree to which a website, brand, or piece of content is surfaced, cited, or discussed by large language model (LLM) powered search tools and chat assistants when users query relevant topics. Unlike traditional search visibility, which measures ranking position on a SERP, AI visibility captures citation rate, prominence in AI-generated answers, and the accuracy of how a model characterizes the site. As AI answer engines displace clicks from traditional search, AI visibility is becoming a primary KPI for content and SEO teams.Read the full guide →
Answer Engine Optimization (AEO)
Answer engine optimization (AEO) is the practice of structuring and writing content so that AI-powered answer engines are more likely to select it as a source when generating responses. AEO extends traditional SEO by emphasizing direct, unambiguous answers to specific questions, clear entity definitions, authoritative attribution, and formats (lists, tables, short paragraphs) that are easy for language models to parse and cite. AEO and GEO are closely related terms; AEO tends to focus on the query-answer format, while GEO is a broader umbrella covering all LLM distribution channels.Read the full guide →

C

Citation
In the context of AI search, a citation is an explicit reference to a source URL or domain name within an AI-generated response. Citations serve as the AI analog of an organic search ranking: they signal that the model considers the cited source authoritative or directly relevant to the query. Not all AI models cite sources by default — ChatGPT's base mode does not, while Perplexity and Google AI Overviews always do — so citation rate metrics vary by model. Earning citations is the primary goal of GEO strategy.
Claude
Claude is a family of large language models developed by Anthropic, available via API and at claude.ai. Claude models (including Haiku, Sonnet, and Opus tiers) are widely used in enterprise AI assistants and are integrated into third-party tools. For GEO practitioners, Claude is a distinct AI channel with its own training data cutoff, retrieval behavior, and citation tendencies. Optimizing for Claude visibility may differ from optimizing for ChatGPT because the models were trained on different corpora with different weighting of source authority.
Core Web Vitals (for AI)
Core Web Vitals for AI is an emerging concept referring to the technical and content quality signals that determine whether an AI model's retrieval system can successfully access, parse, and trust a webpage. While Google's traditional Core Web Vitals (LCP, INP, CLS) measure user-facing performance, the analogous signals for AI include: whether the page renders its primary content in HTML (not JavaScript-only), whether the content is structured with semantic HTML5 elements, whether key facts are stated early and unambiguously, and whether the page loads without authentication walls. Pages that fail these AI accessibility checks are less likely to be cited even if they contain the correct information.

E

Entity Authority
Entity authority is the degree to which a knowledge graph or AI model confidently recognizes and associates a named entity — a brand, person, organization, product, or concept — with specific, accurate attributes. A brand with high entity authority is more likely to be cited correctly and unprompted in relevant AI responses, because the model has seen consistent, corroborating information about that entity across multiple authoritative sources. Building entity authority involves creating clear structured data, earning third-party mentions, maintaining a Wikipedia or Wikidata presence, and ensuring your About/Company pages contain unambiguous entity signals.Read the full guide →

G

Gemini
Gemini is Google's family of multimodal large language models, successor to PaLM and Bard. Gemini powers Google AI Overviews, the Gemini chat product at gemini.google.com, and the AI features in Google Workspace. Because Gemini is directly integrated with Google Search's index and Knowledge Graph, it has unusually broad access to fresh web content compared to models without live retrieval. For GEO, Gemini's citation behavior is especially important because high AI Overviews visibility directly affects Google SERP real estate.
Generative Engine Optimization (GEO)
Generative engine optimization (GEO) is the discipline of optimizing digital content and web presence so that large language model (LLM) powered search engines and AI assistants are more likely to surface, cite, and accurately represent a brand or site in their generated responses. GEO is the AI-era analog of search engine optimization (SEO). Where SEO focuses on ranking signals like backlinks, keywords, and page speed for traditional crawlers, GEO focuses on citability, entity clarity, structured data, and content formats that language models can reliably extract and attribute. GEO encompasses strategies for all major AI answer channels: Google AI Overviews, Perplexity, ChatGPT Search, Claude, and others.Read the full guide →
GEO Citation Rate
GEO citation rate is the percentage of AI model responses, for a given keyword or topic, in which a specific domain is cited as a source. For example, if Bingly tests 100 queries related to "project management software" across five AI models and a domain appears as a citation in 23 of those responses, its GEO citation rate for that topic is 23%. Citation rate is the primary quantitative KPI in GEO, analogous to click-through rate in traditional SEO. Tracking citation rate over time reveals whether optimization efforts are improving AI visibility.
GPT-4
GPT-4 is a large language model developed by OpenAI, released in March 2023, that underpins ChatGPT's paid tiers and the OpenAI API. Successive versions (GPT-4o, GPT-4 Turbo, o1, o3) extended its capabilities with multimodality, longer context windows, and improved reasoning. In the context of GEO, GPT-4-class models are a critical channel because ChatGPT has the largest user base of any AI assistant. When ChatGPT Browse or ChatGPT Search is enabled, the model can retrieve and cite live web content; without retrieval, it draws only on training data, making recency and inclusion in training corpora relevant optimization factors.
Grounding
Grounding is the process by which an AI language model connects its generated output to verifiable, external sources of information — typically retrieved documents, a knowledge base, or search results. A grounded response cites specific sources and constrains its claims to what those sources support, reducing hallucination. For GEO, grounding matters because only content that enters the retrieval pipeline has a chance to be cited: ungroundable content (paywalled, JavaScript-rendered, poorly structured) is excluded regardless of its quality. Google calls its grounding system "Search as a Tool" and Perplexity calls it online mode.

K

Knowledge Graph
A knowledge graph is a structured database that represents entities (people, places, organizations, products, concepts) and the relationships between them as a graph of nodes and edges. Google's Knowledge Graph, built from sources including Wikipedia, Wikidata, and structured data markup, informs both traditional SERP features (Knowledge Panels) and AI model responses. For GEO, appearing in a knowledge graph is a strong signal of entity authority and increases the likelihood that AI models will cite a brand accurately. Brands can influence their knowledge graph presence through structured data on their own pages, Wikidata entries, and consistent entity signals across the web.

L

LLM SEO
LLM SEO (also written LLM-SEO) is a shorthand term for the set of content and technical practices that improve a site's visibility in large language model responses, equivalent in intent to GEO. The term emphasizes the model layer — the fact that it is the language model itself, not just the retrieval index, that must "understand" and favor a site. LLM SEO strategies include writing clearly attributed facts, using entity-rich prose, avoiding thin or ambiguous content, and publishing in formats that tokenize cleanly. Some practitioners use LLM SEO interchangeably with GEO; others reserve LLM SEO for organic training data optimization and GEO specifically for retrieval-augmented answer engines.Read the full guide →
llms.txt
llms.txt is a proposed web standard (analogous to robots.txt and sitemap.xml) that lets website owners provide a structured, concise summary of their site's content specifically for consumption by large language models. The file, placed at the root of a domain as /llms.txt, contains a brief description of the site, its key topics, and links to its most important pages in a Markdown format optimized for LLM token efficiency. Although not yet an official standard, llms.txt has been adopted by a growing number of developer-focused and SaaS sites as an early GEO best practice. AI crawlers that respect the file can build more accurate representations of the site.Read the full guide →

P

Perplexity
Perplexity is an AI-native search engine that combines real-time web retrieval with language model generation to produce cited, conversational answers to user queries. Unlike ChatGPT, Perplexity always retrieves live sources before generating a response, and every answer includes inline citations with source URLs. This makes Perplexity one of the most measurable AI visibility channels: if a domain is indexed and relevant, it has a clear path to citation. Perplexity's "Perplexity Pages" feature also allows brands to publish long-form content directly on the platform.

R

Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) is an AI architecture in which a language model's response is informed by documents retrieved from an external corpus at inference time, rather than relying solely on the model's parametric (trained) knowledge. In a RAG pipeline, a query is first used to fetch relevant documents from an index, those documents are inserted into the model's context window, and the model generates a response grounded in the retrieved content. Most AI answer engines — including Perplexity, Google AI Overviews, and ChatGPT Search — use RAG or a functionally equivalent approach. For GEO, RAG means that a site can gain AI visibility even if it postdates a model's training cutoff, as long as it is crawlable and indexed.
Reinforcement Learning from Human Feedback (RLHF)
Reinforcement learning from human feedback (RLHF) is a training technique in which a language model's outputs are rated by human evaluators, and those ratings are used to fine-tune the model toward producing responses that humans prefer. RLHF shapes which types of sources, writing styles, and answer formats a model favors — meaning that content written in a style that human raters rate as clear, trustworthy, and well-sourced is indirectly more likely to be cited by RLHF-trained models. For GEO practitioners, understanding RLHF is useful context for why authoritative, clearly written, fact-dense content tends to perform better in AI answers.

S

Semantic HTML
Semantic HTML refers to the use of HTML5 elements whose tag names convey meaning about the content they contain — such as <article>, <section>, <h1>–<h6>, <main>, <nav>, <aside>, <figure>, and <time> — as opposed to generic <div> and <span> containers. For GEO, semantic HTML is a technical foundation: AI crawlers and retrieval systems use these structural signals to understand which content is primary, how information is organized, and where headings and definitions appear. A page with well-structured semantic HTML is significantly more parseable by both traditional search crawlers and AI retrieval systems than an equivalent page built with div soup.
Structured Data / Schema.org
Structured data refers to machine-readable markup — most commonly JSON-LD following the Schema.org vocabulary — embedded in a webpage to explicitly declare what entities the page describes and what their properties are. Schema.org types relevant to GEO include Article, FAQPage, HowTo, Product, Organization, Person, and DefinedTerm. Structured data reduces ambiguity for both traditional search crawlers and AI retrieval systems: a page that says <script type="application/ld+json">{"@type":"FAQPage",...}</script> is unambiguously a FAQ, making it easier for a model to extract Q&A pairs and cite them accurately.

T

Training Data
Training data is the corpus of text (and other media) on which a language model's weights are learned during the pre-training phase. For most large models, training data includes a broad crawl of the public web, filtered and deduplicated, as well as curated sources like books, academic papers, and code repositories. A site's presence in training data is a baseline factor in whether a model "knows" about it organically — independent of any live retrieval. However, training data has a cutoff date, and models' training corpora are not publicly disclosed, so practitioners cannot rely on training data presence alone. Combining training data signals (brand mentions, Wikipedia presence) with retrieval optimization (RAG channels) gives the most comprehensive GEO coverage.

Z

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