AEO & GEO (AI Search) Glossary

AEO and GEO are the practices of making content easy for AI answer engines to find, understand and cite. This glossary explains more than 80 AI search terms from A to Z in plain language, each tagged Beginner, Intermediate or Advanced, from AI Overviews and RAG to citations, AI crawlers and llms.txt.

Updated 20 Oct 2026 Covers LLMs, RAG and AI crawlers Free to use, no sign-up

Search the AEO and GEO glossary

AI search terms from A to Z

ATerms starting with A

AI Crawler: A bot that fetches web content for AI systems. Some collect data to train models (such as GPTBot), some gather pages for AI search results, and some fetch a page when a user asks. Site owners can allow or block each type separately in robots.txt.
AI Mode: Google’s conversational, AI-driven search experience, which answers longer and more complex questions and can break a question into several searches behind the scenes.
AI Overviews: AI-generated summaries that Google shows above traditional search results, combining information from several sources and linking to them.
AI Referral Traffic: Website visits that arrive from AI assistants and answer engines, such as ChatGPT or Perplexity. It can be tracked in analytics by its referrer source.
AI Search Visibility: How often and how prominently a brand or website appears in AI-generated answers. It is a newer counterpart to traditional search rankings.
Answer Box: A direct on-page answer shown at the top of results, whether a traditional featured snippet or a newer AI-generated summary.
Answer Engine: A search or assistant experience, such as ChatGPT, Perplexity or Google AI Overviews, built to answer a question directly instead of only listing links.
Answer Engine Optimization (AEO): Structuring and writing content so it is more likely to be selected as a direct, cited answer by AI-driven answer engines.
Answer-First Content: Content that states the direct answer at the start of a section, then adds detail and evidence. It is easy for readers to scan and for AI systems to lift and cite.
Attention Mechanism: A core part of transformer models that lets an AI weigh which words or pieces of context matter most when generating a response.
Augmented Generation: See RAG (Retrieval-Augmented Generation).

BTerms starting with B

Bidirectional Context: The ability of some language models to consider the words both before and after a given word when working out its meaning, which improves comprehension.
Brand Mention (AI Context): An instance where a brand name appears in an AI-generated answer, whether or not it links back to the source.

CTerms starting with C

Chatbot: A conversational AI interface that answers questions in natural language, from simple rule-based tools to advanced assistants powered by large language models.
ChatGPT Search: The web search feature of ChatGPT, which looks up current information online and answers with links to its sources.
Chunking (Content Chunking): Breaking a document into smaller, self-contained sections so an AI system can retrieve and cite the most relevant passage rather than a whole page.
Citation (AI Citation): A reference or link to a source inside an AI-generated answer. It is similar in spirit to a backlink, though it works differently and may not pass ranking value.
Citation Density: How often a source is cited across AI-generated answers on related topics, used as a rough sign of perceived authority.
Context Window: The maximum amount of text an AI model can consider at once when generating a response, which limits how much source material it can use.
Conversational AI: AI systems designed to interact through natural, multi-turn dialogue rather than single, isolated queries.
Conversational Search: Searching through natural, back-and-forth dialogue with an assistant, rather than typing separate keyword queries into a search box.
Cosine Similarity: A mathematical measure of how close two pieces of content are in meaning, used by AI systems to find relevant passages during retrieval.

DTerms starting with D

Data Freshness: How recently content was published or updated, an important factor in whether AI systems treat it as current and trustworthy.
Dense Retrieval: A search method that finds relevant content by comparing meaning, through embeddings, rather than matching exact keywords.
Digital Assistant: A voice or text-based AI tool, such as Siri, Alexa or Google Assistant, that helps people complete tasks or answer questions.
Direct Answer: A short, standalone response to a query generated by an AI system, which often removes the need to click through to a website.

ETerms starting with E

E-E-A-T (AI Trust Context): Experience, Expertise, Authoritativeness and Trustworthiness signals. Search systems and, in practice, AI answer engines favor sources that show them, although the exact way AI systems pick sources is not public.
Embeddings (Vector Embeddings): Numerical representations of words, sentences or documents that capture their meaning, so AI systems can compare and retrieve content by semantic similarity.
Entity Recognition: The process by which an AI system identifies and classifies real-world people, places, organizations and things mentioned in text.
Extractive Summarization: A summarization method that pulls exact sentences or phrases from source content, rather than writing new wording.

FTerms starting with F

Fan-Out Query: See Query Fan-Out.
Few-Shot Learning: A technique where an AI model gets a small number of examples inside a prompt to guide its response, without extra training.
Fine-Tuning: Further training a pre-built AI model on a narrower dataset to improve its performance on a particular task or field.

GTerms starting with G

GEO (Generative Engine Optimization): Optimizing content, structure and authority signals so it is more likely to be retrieved, referenced and cited by generative AI systems such as AI Overviews and chat assistants.
Generative Search: A search experience where the result is a newly generated, synthesized answer rather than a static list of ranked links.
Google-Extended: A robots.txt token that lets site owners control whether their content is used for Google’s Gemini models and AI grounding. Blocking it does not affect how a site appears in Google Search.
GPTBot: OpenAI’s crawler for gathering public content to train its models. OpenAI uses other bots for ChatGPT search and user requests, and site owners can allow or block each in robots.txt.
Grounding: Anchoring an AI model’s response in specific, verifiable source content, which reduces the risk of made-up or inaccurate answers.

HTerms starting with H

Hallucination: When an AI model produces information that sounds plausible but is factually wrong or invented, not grounded in real source material.
Hybrid Search: A retrieval approach that combines traditional keyword matching with semantic, meaning-based search to improve relevance.

ITerms starting with I

Indexing for AI: How AI systems and their crawlers discover, process and store web content so it can be retrieved later to generate answers.
Information Retrieval: The field concerned with finding relevant content in a large collection of documents in response to a query. It underlies both search engines and AI answer systems.
Intent Modeling: How an AI or search system interprets the goal behind a query so it can give a genuinely useful response.

JTerms starting with J

JSON-LD (AI Readability Context): A structured data format that gives search engines and AI crawlers clear, machine-readable facts about a page. Google says AI features need no special markup, but accurate structured data still helps machines understand your content.

KTerms starting with K

Knowledge Cutoff: The date after which an AI model has no built-in knowledge, because its training data only runs up to a fixed point in time.
Knowledge Graph (AI Context): A structured database of real-world entities and their relationships that AI systems can use to ground and check answers, beyond raw text prediction.

LTerms starting with L

Large Language Model (LLM): An AI model trained on huge amounts of text that can understand and generate human-like language, such as GPT, Gemini and Claude.
LLM Crawler: See AI Crawler.
llms.txt: A proposed file, similar to robots.txt, that would give AI systems a curated summary of a site’s most important content. Adoption is limited, and no major AI search engine has confirmed that it uses the file.
Long-Context Window: A model capability that lets it process and reference very large amounts of text at once, useful for grounding answers in extensive source material.

MTerms starting with M

Machine-Readable Content: Content structured, through clean HTML, schema markup or clear formatting, so automated systems can parse and understand it as well as human readers can.
Model Context Protocol (MCP): An open standard that lets AI models connect to external tools, data sources and services in a consistent, structured way.
Multimodal AI: AI systems that can understand and generate more than one type of content, such as text, images and audio, within a single model.
Multi-Turn Conversation: An exchange where a user and an AI assistant trade several messages, with the AI keeping context from earlier in the conversation.

NTerms starting with N

Natural Language Generation (NLG): The AI process of producing coherent, human-readable text as output. It is how chat assistants write their responses.
Natural Language Query: A search or question phrased the way a person would naturally say it, rather than as short, fragmented keywords.
Neural Search: Search powered by neural networks that ranks results by learned semantic similarity rather than exact keyword matches.

OTerms starting with O

Open Web Crawling: The ongoing process by which AI companies’ bots access publicly available websites to gather content for training or real-time retrieval.

PTerms starting with P

Passage Ranking: Evaluating and ranking specific sections of a document, rather than the whole page, for relevance, so the most useful passage can be surfaced or cited.
Perplexity (AI Search Engine): An AI-powered answer engine that writes direct, cited responses by combining information from multiple web sources.
Prompt: The input text a user gives an AI system, which the model interprets to generate its response.
Prompt Engineering: Carefully crafting prompts to get more accurate, useful or specific outputs from an AI model.
Prompt Injection: A technique, often malicious, where hidden instructions are placed in content to manipulate how an AI system processes or answers.

QTerms starting with Q

Query Fan-Out: A retrieval technique where an AI system splits one question into several related sub-queries, to gather broader source material before answering.
Query Understanding: How a search or AI system interprets the meaning, context and intent behind a user’s input.

RTerms starting with R

RAG (Retrieval-Augmented Generation): An AI architecture that retrieves relevant documents from an external source before generating a response, grounding answers in real content rather than only the model’s training data.
Ranking Signal (LLM Context): Any factor, such as authority, freshness or structure, that may influence whether and how prominently a source is used in an AI-generated answer.
Retrieval Corpus: The full set of documents or data an AI system can search through when retrieving information to answer a query.

STerms starting with S

Semantic Chunking: See Chunking.
Semantic Search: Search based on the meaning and context behind a query rather than exact keyword matching.
SGE (Search Generative Experience): Google’s original name for its AI-generated search summaries, which it later rolled out more broadly as AI Overviews.
Share of Model: A brand’s relative visibility and citation frequency across AI assistants and answer engines, an AI-era counterpart to share of voice.
Source Attribution: See Citation.
Source Diversity: How many distinct, varied sources an AI system draws on when building an answer, rather than relying on just one or two.
Summarization: The AI process of condensing longer content into a shorter version while keeping its key information.
Synthetic Query: An artificially generated search query, often used to test or train AI and search systems without relying only on real user data.

TTerms starting with T

Token: A chunk of text, roughly a word or part of a word, that an AI model processes as its basic unit of input and output.
Token Limit: The maximum number of tokens a model can process in a single input and output, which caps how much text it can read or write at once.
Training Data: The large body of text and other content an AI model learns from during development, which shapes what it knows and how it responds.
Transformer Model: The neural network design behind most modern LLMs, which uses attention mechanisms to process and generate language.

UTerms starting with U

User Intent Modeling: See Query Understanding.

VTerms starting with V

Vector Database: A specialized database that stores content as embeddings, so AI systems can search quickly by meaning rather than exact text.
Vector Embedding: See Embeddings.
Voice Assistant: An AI tool that responds to spoken queries, such as Siri, Alexa or Google Assistant, often built on similar language understanding technology as chat-based AI.

WTerms starting with W

Weights (Model Weights): The internal numerical parameters an AI model learns during training, which decide how it processes input and produces output.

XTerms starting with X

xAI (Grok): An AI chatbot and answer engine developed by xAI and integrated into the X platform, formerly Twitter.

YTerms starting with Y

YMYL (AI Context): Your Money or Your Life topics, such as health, finance and safety, where wrong answers can cause real harm. AI systems and search engines hold them to a higher accuracy and trust standard.

ZTerms starting with Z

Zero-Click AI Answer: A query fully answered inside an AI-generated response, so the user gets what they need without visiting any external website.
Zero-Shot Learning: An AI model’s ability to complete a task or answer a question it was not specifically trained on, using only its general knowledge and the prompt it is given.

What are AEO and GEO, and how do you use this glossary?

AEO (answer engine optimization) and GEO (generative engine optimization) are the practices of making content easy for AI answer engines, such as Google AI Overviews, ChatGPT and Perplexity, to find, understand, trust and cite. They build on SEO, adding clear answers, structured content and strong authority signals.

Start with the Beginner terms, such as answer engine, LLM, hallucination, citation and prompt. Move to Intermediate terms as you plan content for AI search, and use the Advanced terms for retrieval, embeddings and crawler controls.

Search by term or filter by level above. Related terms link to each other, so you can follow an idea from one concept to the next.

How AI search answers a question

1. UnderstandWorks out the intent behind the question
2. RetrieveFinds relevant passages, often by searching several related queries
3. SelectRanks passages and picks trustworthy sources
4. GenerateWrites an answer and, usually, cites its sources

How do SEO, AEO and GEO differ?

They overlap heavily. SEO is the foundation, and AEO and GEO extend it for answers that AI systems write.

The practices overlap, and good SEO supports both AEO and GEO.
Practice	Main goal	Where it shows up	Key tactics
SEO	Rank pages and earn clicks	Traditional search results	Technical health, content, links, local signals
AEO	Be chosen as the direct answer	Featured snippets, voice answers, answer boxes	Answer-first writing, FAQs, clear headings, schema
GEO	Be cited inside AI-written answers	AI Overviews, ChatGPT, Perplexity, Gemini	Original research, entity clarity, citations, crawler access

AEO and GEO questions, answered

Quick answers to the most searched AI search basics.

What is AEO?

AEO, or answer engine optimization, is structuring and writing content so it can be chosen as a direct answer by search features and AI assistants. It relies on answer-first writing, clear headings, question-based sections, FAQs and accurate structured data.

What is GEO?

GEO, or generative engine optimization, is making content more likely to be retrieved and cited in answers that generative AI writes, such as Google AI Overviews, ChatGPT and Perplexity. It relies on clear, credible, well-structured content and strong brand and authority signals.

What is the difference between SEO, AEO and GEO?

SEO aims to rank pages in search results. AEO aims to have your content chosen as the direct answer. GEO aims to have your brand cited in AI-written answers. They overlap heavily, and strong SEO is the foundation for both AEO and GEO.

Does SEO still matter for AI search?

Yes. AI search features rely on the same quality signals and often on search indexes, so crawlable, fast, helpful and trustworthy pages remain the foundation. Google says its AI features need no special optimization beyond SEO best practices.

How do I get cited in AI Overviews and ChatGPT?

No one can guarantee a citation. You improve your odds by answering questions clearly near the top of the page, covering a topic thoroughly, showing real expertise, publishing original data, keeping content fresh, and letting the right crawlers reach your pages.

Should I block AI crawlers?

It depends on your goals. Training crawlers and search crawlers are separate, so you can block model training while still allowing AI search bots. Blocking search-related bots can reduce your chances of being cited. Use robots.txt to set each one deliberately.

What is llms.txt, and do I need it?

llms.txt is a proposed file that would give AI systems a curated summary of your site. Adoption is limited, and no major AI search engine has confirmed it uses the file. It is harmless to add, but it is not a substitute for good content and SEO.

Does schema markup help with AI search?

Structured data helps machines understand who you are and what a page is about, which supports both search and AI systems. Google says AI features need no special markup, so treat schema as good hygiene rather than a guarantee of citations.

How do I track my visibility in AI search?

Check referral traffic from AI assistants in your analytics, and test your key questions in ChatGPT, Perplexity and Google AI Overviews to see whether your brand is mentioned or cited. Repeat the tests regularly, because AI answers change from day to day.

Is this AEO and GEO glossary free?

Yes. It is completely free to read, with no sign-up. You can search it, filter it by level and jump to any letter.

Explore our other glossaries

Looking for SEO, PPC, social media or content terms? Each has its own dedicated glossary.

Browse all glossariesThe full hub of 12 glossaries and 1,000+ terms. SEO GlossarySEO terms from on-page to technical SEO. Content MarketingEditorial, writing and content strategy terms. PPC and Google AdsBidding, targeting and campaign terms. Social Media GlossaryInstagram, TikTok and paid social terms. Performance MarketingAttribution, growth and cross-channel terms. Web Design and DevelopmentDesign, front-end, back-end and CMS terms. Email MarketingDeliverability, automation and campaign terms. Analytics and ReportingGA4, statistics and dashboarding terms. E-Commerce MarketingCheckout, fulfillment and retention terms. Branding and IdentityBrand strategy, visual identity and positioning. Marketing AutomationWorkflow, CRM integration and orchestration.

Put the terms to work: set your AI crawler rules with our robots.txt generator, add structured data with our schema generators, or get a free audit through our Mumbai SEO services page.

Did not find the term you were looking for?

Get in touch and we will add it, or walk you through what it means for your business.

Talk to our team See our services