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The AI Visibility & GEO glossary
Definitions, frameworks, and tactics for marketers optimizing for ChatGPT, Claude, Perplexity, and Google AI Overviews. Updated monthly as the field evolves.
Generative engine optimization
11 termsAI brand mentions
AI brand mentions are the instances of your brand name appearing inside responses generated by AI assistants — ChatGPT, Claude, Gemini, Perplexity, Grok, and Google AI Overviews. Unlike traditional brand monitoring across social and press, AI mentions surface inside the answer a buyer is reading, making them a high-leverage demand signal for Generative Engine Optimization (GEO).
AI citation optimization
AI citation optimization is the practice of structuring web content so AI assistants — ChatGPT, Claude, Perplexity, Gemini, Bing Chat, and Google AI Overviews — choose to cite it as a source in their generated answers. It is the citation-layer counterpart to traditional SEO link building and a core discipline within Generative Engine Optimization (GEO).
AI content ranking
AI content ranking is the relative position your content holds in AI-generated answers — first-cited, mid-list, or never surfaced — across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. Where traditional SEO ranking is a numbered position in a SERP, AI content ranking is order-of-mention and citation-prominence inside a synthesized answer.
AI content strategy
AI content strategy is the deliberate plan for producing, structuring, and maintaining content so it earns visibility inside AI assistants — ChatGPT, Claude, Perplexity, Gemini, Grok, and Google AI Overviews. It rebuilds traditional editorial planning around the way LLMs choose, cite, and synthesize sources rather than the way Google ranks links.
AI search visibility
AI search visibility is the umbrella metric capturing how often, how prominently, and how favorably your brand appears across AI assistants — ChatGPT, Claude, Perplexity, Gemini, Grok, and Google AI Overviews. It bundles mentions, citations, ranking position, sentiment, and AI-referred traffic into the executive-level read of a brand's standing in AI search.
AI visibility score
The AI visibility score is a single composite number — typically on a 0–100 scale — that summarizes a brand's standing across AI assistants (ChatGPT, Claude, Gemini, Perplexity, Grok, AI Overviews) by blending mention frequency, citation rate, ranking position, sentiment, and AI-referred traffic. It is the executive-friendly headline metric for Generative Engine Optimization (GEO) programs.
Brand authority
Brand authority is the composite signal — built from secondary-source mentions, structured presence on trusted directories, original research, and consistent on-brand publishing — that AI assistants use to decide whether to cite, mention, or ignore your brand. In Generative Engine Optimization (GEO), brand authority is the prior probability the model brings to your domain before it ever evaluates a specific page.
Citation probability
Citation probability is the likelihood that an AI system will cite a specific URL when generating a response to a target prompt. Unlike share of model, which measures brand visibility across a prompt set, citation probability is a per-URL metric — it tells you how strong an individual page is at earning citations.
Content freshness
Content freshness is how recently a page was published or substantively updated, as signaled to AI assistants and search engines through `dateModified`, visible publish dates, and changed body content. Retrieval-grounded AI engines — Perplexity, Google AI Overviews, Bing Chat, Gemini — weight freshness heavily when choosing citation sources for time-sensitive queries.
Generative engine optimization (GEO)
Generative engine optimization (GEO) is the practice of structuring content and brand presence so that AI systems like ChatGPT, Claude, Perplexity, and Google AI Overviews cite, quote, or recommend it when generating answers. Unlike traditional SEO, which competes for ranked positions in a list of links, GEO competes for inclusion inside the answer itself.
Share of model
Share of model is the percentage of relevant AI-generated answers in which your brand appears, measured across a defined set of prompts and platforms. It is the AI-search equivalent of share of voice and the headline metric for tracking GEO performance.
Answer engine optimization
3 termsAnswer engine optimization (AEO)
Answer engine optimization (AEO) is the practice of structuring content so that search platforms select it as the direct answer to a user query — whether that answer surfaces in a Google featured snippet, a voice assistant response, an AI Overview, or an LLM chat reply. Where SEO competes for ranked links, AEO competes for the answer itself.
Featured snippets
A featured snippet is a highlighted answer box that appears at the top of Google search results — above the standard organic listings — pulled directly from a web page. It answers the user's query in 40–60 words without requiring a click, and the same content often feeds Google AI Overviews and voice assistant responses.
Zero-click search
A zero-click search is a Google query that ends without the user clicking any organic or paid result. The user's question is resolved directly on the search results page through featured snippets, knowledge panels, AI Overviews, or other SERP features. In 2026, roughly 60–65% of all Google searches in the US end this way.
Search engine optimization
11 termsContent gap analysis
Content gap analysis is the systematic comparison of your site's content coverage against competitors and against the queries your audience actually searches — surfacing topics where competitors rank or earn AI citations and you don't. In 2026 it expands beyond Google rankings to include AI search gaps — topics where ChatGPT, Claude, Perplexity, and AI Overviews cite competitors but never mention you.
E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness)
E-E-A-T is the quality framework Google uses in its Search Quality Rater Guidelines to evaluate web content. The four pillars are Experience (firsthand involvement with the topic), Expertise (depth of knowledge), Authoritativeness (external recognition), and Trustworthiness (accuracy and transparency). E-E-A-T is not a direct ranking factor — but the signals it measures train the algorithms that are.
Google BERT algorithm
The Google BERT algorithm is a natural-language model — Bidirectional Encoder Representations from Transformers — that Google rolled into Search in October 2019 to better interpret the full context of a query rather than reading it word-by-word. BERT is now part of the foundation that AI Overviews and AI Mode build on, making it the bridge between traditional SEO and 2026's generative search.
Google core updates
Google core updates are broad, system-wide changes to Google Search's ranking algorithms, rolled out 2–4 times a year and named by month (e.g. "March 2024 core update", "November 2025 core update"). They re-evaluate site-level quality and topical authority, often shifting traffic across millions of domains for weeks while the rollout completes.
Internal linking
Internal linking is the practice of linking from one page on your domain to another. Internal links pass link equity, define topical relationships, and shape the crawl path for both Google and AI crawlers (GPTBot, ClaudeBot, PerplexityBot, Google-Extended). Strong internal linking is one of the highest-leverage on-page levers for both SEO and Generative Engine Optimization (GEO).
Keyword clustering
Keyword clustering is the practice of grouping related queries into topical clusters that map to a single page or content asset — instead of building one page per individual keyword. Clustering is what turns a 5,000-keyword research dump into a 20-cluster content roadmap and is foundational to both modern SEO and Generative Engine Optimization (GEO).
Keyword research
Keyword research is the practice of identifying the queries your audience actually types into Google, Bing, and AI assistants — with their volume, intent, difficulty, and competitive landscape — to ground content investment in real demand. In 2026, modern keyword research extends beyond head-term and long-tail keywords to include *prompts*: the conversational queries buyers send to ChatGPT, Claude, Perplexity, and AI Mode.
Knowledge panel
A knowledge panel is the right-side (desktop) or top-of-results (mobile) information card Google renders for entities — brands, people, places, products — drawn from its Knowledge Graph. Knowledge panels signal that Google recognizes the entity, and the same entity recognition feeds AI Overviews, AI Mode, and many AI assistants' brand understanding.
Schema markup
Schema markup is structured data added to web pages using the schema.org vocabulary that tells search engines and AI systems exactly what the content represents — a product, an article, a recipe, an FAQ, a person. It powers rich results in Google, drives entity understanding in knowledge graphs, and increasingly determines whether content is cited in AI Overviews and LLM-generated answers.
Search intent
Search intent is the underlying goal behind a query — what the user is actually trying to accomplish when they search. Classifying intent is the foundation of modern SEO and AI search optimization because the right answer for an informational query ("what is share of voice") is structurally different from the right answer for a transactional query ("buy AI visibility tracking software").
SERP analysis
SERP analysis is the systematic study of a search engine results page for a target query — the ranked links, AI Overviews, People Also Ask boxes, knowledge panels, video carousels, and ads — to understand what Google thinks the user wants and what content format is winning. In 2026, SERP analysis has expanded to include AI Mode citations and AI Overview source lists alongside the traditional ten blue links.
AI & LLMs
14 termsAI agent
An AI agent is a software system that uses a large language model (typically GPT-4o, Claude 3.5 / 4 Sonnet, Gemini 2.5, or open-source equivalents) to plan, decide, and act over multiple steps to complete a goal — calling tools, retrieving data, and producing outputs without step-by-step human supervision. Agents are the working surface of agentic AI in 2026.
AI API
An AI API is a programmatic interface that lets developers send prompts to a large language model and receive generated responses — typically over HTTP with JSON payloads. The major AI APIs in 2026 are the OpenAI API (GPT-4o, GPT-4.1), Anthropic API (Claude 3.5 / 4 Sonnet, Claude Opus), Google Gemini API, xAI Grok API, and the Perplexity API.
AI bots
AI bots are the automated crawlers operated by AI companies to fetch web content for training and retrieval. The major AI bots in 2026 are GPTBot and ChatGPT-User (OpenAI), ClaudeBot and anthropic-ai (Anthropic), PerplexityBot, Google-Extended (Gemini), and Bytespider (ByteDance). Whether your robots.txt allows them determines whether your content can be cited inside AI assistants.
AI grounding
AI grounding is the practice of anchoring an LLM's response in retrieved, citable sources at inference time — instead of letting the model rely solely on its training memory. Grounding is what separates a hallucination-prone chatbot from a search-grade AI assistant like Perplexity, Google AI Overviews, Bing Chat, or retrieval-augmented ChatGPT.
AI hallucination
AI hallucination is when a large language model generates content that sounds plausible and confident but is factually wrong, fabricated, or unverifiable — invented citations, made-up statistics, or fictional events presented with the same fluency as accurate information. Hallucination is a structural feature of how LLMs work, not a bug that can be fully eliminated.
AI indexing
AI indexing is the process by which AI assistants — ChatGPT, Claude, Gemini, Perplexity, Grok, and Google AI Overviews — crawl, parse, embed, and store web content so it can be retrieved and cited at inference time. It is the AI-search counterpart to Google's traditional index, and the gateway any page must pass through to be eligible for citation.
AI inference
AI inference is the runtime step where a trained AI model takes a prompt and produces an output — the tokens you see streaming back from ChatGPT, Claude, Gemini, or Perplexity. Inference is what costs money in production: every prompt and every generated token consumes GPU time, and the economics of any AI product live in this loop.
AI Mode
AI Mode is Google Search's dedicated generative-answer surface, rolled out broadly in 2025–2026 as a tab that runs the user's query through Gemini-powered retrieval and synthesis instead of (or alongside) the traditional ranked-link SERP. It is the most consumer-visible expression of Google's transition from links to answers.
AI models for deep research
AI models for deep research are the long-running, agentic modes shipped by major AI providers — ChatGPT Deep Research, Perplexity Deep Research, Gemini Deep Research, and Claude's research mode — that take a single complex prompt, autonomously plan and run dozens of web searches, read source pages end-to-end, and synthesize a multi-page report with full citations. They are the most agentic search experience exposed to consumers in 2026.
AI Overview
AI Overview is Google's AI-generated answer feature that appears at the top of search results, synthesizing information from multiple web sources into a single response with inline citations. Powered by Gemini and using query fan-out to retrieve from across the web, AI Overviews now appear on roughly 48% of US Google searches and have fundamentally restructured organic visibility.
AI regulation
AI regulation is the body of laws, executive orders, and enforcement frameworks governing how AI systems are built, trained, deployed, and audited. The 2026 landscape is dominated by the EU AI Act (in active enforcement), the US Executive Order on AI, the UK's pro-innovation framework, and a fast-growing set of state-level laws in California, Colorado, and New York.
AI training data
AI training data is the corpus of text, code, images, and other content used to train large language models. Frontier models like GPT-4o, Claude 4 Sonnet, Gemini 2.5, and Llama 4 are trained on trillions of tokens drawn from web crawls, books, code repositories, and licensed datasets — the composition of which shapes what the model knows, who it cites, and how it represents brands.
llms.txt
llms.txt is a proposed web standard — a markdown-formatted file placed at the root of a website — that gives LLMs and AI tools a curated index of a site's most important content. Modeled on robots.txt and sitemap.xml but designed for LLM comprehension rather than search crawlers, llms.txt is in the early adoption phase as of 2026, with no major AI platform officially committed to consuming it.
Retrieval-augmented generation (RAG)
Retrieval-augmented generation (RAG) is an AI architecture that gives a large language model real-time access to external documents at query time — retrieving relevant passages from a vector database or search index and inserting them into the model's context before it generates a response. RAG is the foundation of modern AI search and the most effective technique for reducing hallucination.
Brand visibility & analytics
3 termsAI share of voice
AI share of voice is your brand's proportion of mentions in AI-generated responses relative to competitors, measured across a defined set of prompts and platforms. It adapts the traditional share of voice metric for AI search — where visibility lives inside chat answers and AI Overviews rather than ranked links or media impressions.
AI-referred traffic
AI-referred traffic is the visits a website receives from users who clicked through from an AI assistant — ChatGPT, Claude, Perplexity, Gemini, Grok, Copilot, or Google AI Overviews. It is the bottom-of-funnel proof that AI visibility work is converting into real sessions, signups, and revenue, not just citations on a chart.
Sentiment monitoring
Sentiment monitoring is the practice of continuously analyzing the tone AI assistants use when describing your brand — positive, neutral, or negative — across ChatGPT, Claude, Gemini, Perplexity, and Grok. Unlike social-media sentiment, the audience is the AI model itself, and a negative skew can shape how millions of buyers hear your brand described before they ever visit your site.