Answer Engine Optimization (AEO) optimizes content for answer engines that provide direct, often conversational answers instead of a results list.
AEO is often used synonymously with GEO. More narrowly, AEO focuses on question-and-answer formats such as featured snippets, voice assistants, and AI summaries, while GEO covers generative AI systems more broadly.
Example: An FAQ page is written so each question and answer works independently in Google AI Mode or a voice assistant.
An answer engine is a search system that responds to a query with a direct, naturally worded answer instead of a list of links.
Examples include ChatGPT Search, Perplexity, Google AI Mode, and Microsoft Copilot. Answer engines commonly combine web crawling, retrieval-augmented generation, and a language model to create answers and cite sources.
Generative Engine Optimization (GEO) optimizes content and brand presence so AI systems mention, cite, and positively contextualize a brand in generated answers.
GEO extends SEO into a search environment where users increasingly receive generated answers instead of lists of links. It optimizes mentions, citation frequency, answer position, and sentiment rather than traditional ranking positions.
Example: A software company restructures its product pages so ChatGPT names the brand with accurate facts when asked about the best CRM for startups.
A large language model (LLM) is an AI model trained on large amounts of text to understand and generate human-like language.
LLMs such as GPT, Claude, Gemini, and Llama power chatbots and answer engines. For GEO, the key question is how they combine training data and live search results to include brands and facts in answers.
Retrieval-Augmented Generation (RAG) lets a language model retrieve current web or database content before generating an answer.
RAG enables systems such as ChatGPT Search and Perplexity to provide current, sourced answers rather than relying only on training knowledge. For GEO, content must be crawlable, current, and clearly structured to be selected during retrieval.
A zero-click search answers a user's question directly in the search or answer interface without requiring a website visit.
AI summaries and answer engines are increasing the share of zero-click searches. For brands, visibility inside the generated answer therefore becomes more important than click-through rate alone.
AI Visibility describes how often, where, and with what sentiment a brand appears in answers generated by systems such as ChatGPT, Claude, Gemini, or Perplexity.
AI Visibility is the GEO equivalent of traditional search visibility. It is typically measured through recurring prompts sent to multiple AI models to evaluate mentions, citations, positions, and sentiment.
The AI Visibility Score is an aggregate metric summarizing how strongly a brand appears across multiple prompts and AI models.
The score generally combines mention frequency, answer position, and sentiment. It provides a quick comparison similar to a visibility index in traditional SEO tools.
A brand mention occurs when an AI-generated answer names a brand, whether or not it links to or cites a source.
Brand mentions differ from citations: a brand may be named without its website being cited, while its website may be used as a source without the brand appearing in the answer text.
A citation occurs when an AI answer explicitly names or links to a URL or domain as its source.
Citations show whether an AI system treats owned content as a trustworthy source. They differ from used sources that influenced an answer without being shown; together, both signals reveal content authority.
A hallucination is a factually incorrect statement generated by an AI model and expressed with apparent confidence.
For brands, hallucinations matter when AI systems publish incorrect prices, features, or company facts. Monitoring tools can identify these deviations by comparing answers with verified brand information.
Position describes where a brand appears within an AI-generated list or recommendation, comparable to ranking position in traditional search results.
If a brand is listed first among five options in an answer about the best CRM for startups, it holds position one. Earlier positions tend to correlate with stronger awareness and trust.
Sentiment analysis in GEO assesses whether an AI answer presents a brand positively, neutrally, or negatively.
Unlike visibility, which measures whether a brand appears at all, sentiment measures the quality of that mention. A brand can be highly visible yet consistently described as expensive or complex.
Share of Voice (AI) is a brand's percentage of all brand mentions for a topic in AI answers compared with competitors.
If one brand appears in three of ten relevant prompts while a competitor appears in seven, their shares of voice are 30% and 70%. The metric indicates relative competitive position in AI-generated answers.
Content chunking structures information into self-contained sections that an AI system can understand and cite independently.
A paragraph that depends on previous context is harder for retrieval systems to extract than a paragraph containing a complete standalone statement—the principle used throughout this glossary.
An AI crawler is an automated program that visits websites to read content for model training or live AI answers.
Examples include GPTBot, ClaudeBot, PerplexityBot, and Google-Extended. Whether and how often these crawlers visit a site indicates whether its content can become a source for AI answers.
An entity is an identifiable thing—such as a brand, person, product, or place—that AI systems connect across sources in a knowledge graph.
Consistent names, domains, social profiles, and descriptions improve entity clarity and help AI systems identify a brand correctly rather than confuse it with similar terms.
llms.txt is a proposed root-level text file that summarizes a website's important content and intended interpretation for AI systems.
It is often described as a GEO counterpart to robots.txt. It is not an official standard with guaranteed impact, but it is a growing practice for offering AI systems a curated content overview.
Structured Data is machine-readable Schema.org markup that explicitly describes content types such as products, FAQs, definitions, and organizations.
FAQPage, DefinedTerm, DefinedTermSet, Product, and Organization are particularly relevant to GEO because they help answer engines classify and cite content accurately.
AI Visibility Monitoring continuously tracks a brand's visibility over time and identifies changes across AI-generated answers.
Because AI models and their answers vary by time, region, and model, a single test is rarely representative. GEO tools therefore automate daily or weekly tracking.
A GEO Readiness Check assesses whether a website's technical structure, authority, and content make it suitable for recognition and citation by AI systems.
Unlike classic SEO audits, it specifically evaluates content chunking, structured data, entity clarity, and accessibility for AI crawlers.
A GEO tool is software that measures how a brand appears in answers from systems such as ChatGPT, Claude, Gemini, and Perplexity and derives optimization recommendations.
GEO tools repeatedly send defined prompts to several AI models and analyze brand mentions, positions, sentiment, and cited sources. Providers include Peec AI, Profound, Finseo, and PromptScan, which focuses on brands in the DACH market.
Example: A company uses a GEO tool to check daily whether it appears for queries such as 'best [product category] 2026' and improves content based on the results.
Large Language Model Optimization (LLMO) optimizes content for processing and reuse by language models and is often used synonymously with GEO.
GEO emphasizes marketing and visibility, while LLMO is more common in technical contexts such as content structure and training-data optimization. In practice, the two concepts overlap strongly.
Agentic Search is a search paradigm in which AI agents independently perform multi-step tasks such as research, comparison, evaluation, and transactions.