Clear definitions covering generative search, RAG, retrieval, citations and measurement. It includes vocabulary used across our corpus of 369 GEO research studies, with a dedicated page for every concept.
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GEO fundamentals
Core concepts in generative search, answer engines and language models.
GEO
Generative Engine Optimization
GEO is the optimization of a brand and its sources to earn presence, mentions and citations in AI-generated answers. It covers content, authority, technical access, external distribution and measurement.
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AEO
Answer Engine Optimization
AEO prepares content so an answer engine can extract a clear, direct response. It overlaps with GEO, although AEO emphasizes answering questions while GEO focuses on visibility inside generative answers.
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LLM SEO
Search optimization for large language models
LLM SEO is a practical label for work that improves how language models find, understand and represent a brand. In the market it is often used as a rough synonym for GEO.
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AI search
Search assisted or generated by artificial intelligence
AI search returns a synthesized answer, conversation or recommendation rather than only ranking links. It may use model memory, live web search or both.
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Generative engine
A system that retrieves or uses knowledge and generates an answer
A generative engine combines an AI model with search, retrieval or tools to write a new answer. Its output may include citations, links, products, maps or recommendations.
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Answer engine
A system that provides a direct answer to a query
An answer engine tries to resolve the query within its own interface. It may be generative or rely on snippets, knowledge bases and structured results.
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Zero-click search
A query resolved without visiting an external result
A zero-click search ends inside the search or assistant interface because the user gets the needed information there. In AI, brand visibility can exist even when no website session follows.
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AI Overviews
AI-generated summaries in Google Search
AI Overviews are generated answers shown by Google for certain searches and may link to supporting sources. Their composition and sources can vary by query, location and time.
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Google AI Mode
Google’s conversational search experience
Google AI Mode expands search into a conversational experience with generated answers, follow-up questions and source links. It should not be measured as if it were identical to AI Overviews.
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Large language model
LLM
A large language model learns patterns from very large text collections to predict and generate language. It can answer from its parameters or connect to search, RAG and external tools.
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Prompt
An instruction or query sent to a model
A prompt is the text and context that guide a model’s answer. In GEO it represents a search need that can be phrased in many ways and produce different results.
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Branded query
A query that includes a brand or product name
A branded query explicitly asks about a company, product or person. It usually measures recognition and accuracy, not the ability to be discovered among alternatives.
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Non-branded query
A query that expresses a need without naming a brand
A non-branded query asks for options, solutions or comparisons without specifying which company should appear. It is the main surface for measuring discovery and competition in AI answers.
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Retrieval and RAG
How an AI system finds, filters, ranks and uses information before answering.
RAG
Retrieval-Augmented Generation
RAG is an architecture in which a system retrieves relevant documents or passages and adds them to the context before generating an answer. It enables the use of external, recent or private information.
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Retriever
The component that finds candidate documents or passages
A retriever searches an index, the web or a vector database for information related to the query. Its output feeds later reranking and generation stages.
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Generator
The component that writes the final answer
The generator is the model that turns instructions and retrieved context into an answer. It may omit, summarize or combine sources even when they were retrieved correctly.
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Web grounding
The use of web results to support an answer
Web grounding connects generation to pages retrieved in real time. It reduces exclusive reliance on model memory and may produce visible links or citations.
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Grounding
Anchoring an answer in available evidence
Grounding is the connection between generated claims and a source, datum or context that supports them. A well-grounded system can better verify what it says and where it came from.
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Information retrieval
The process of finding information relevant to a query
Retrieval selects documents or passages before an answer is generated. It may use lexical matching, ranking signals, embeddings or a combination.
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Reranking
Reordering candidates after initial retrieval
Reranking scores a reduced set of documents or passages again with a more precise model. A source may be retrieved yet still fall out before reaching the generator.
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Candidate source pool
The group of documents considered before generation
The candidate pool contains sources that passed an initial retrieval stage. If a page does not enter that group, the generator cannot select or cite it.
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Query fan-out
Decomposition of one query into several subqueries
Query fan-out is the process by which an engine expands a complex question into smaller searches. Sources retrieved for those subqueries are later combined into an answer.
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Synthetic query
A query generated by a system to retrieve information
A synthetic query is not written literally by the user: the engine creates it to reformulate, expand or split the need. It may use wording different from the original prompt.
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Semantic search
Retrieval based on meaning rather than only exact words
Semantic search represents queries and documents by meaning to find conceptually related content. It often relies on embeddings and similarity measures.
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Embedding
A numerical representation of content meaning
An embedding converts text, images or other content into a vector. Semantically similar items are placed close together and can be retrieved by similarity.
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Vector database
A system that stores and searches vectors by similarity
A vector database indexes embeddings to retrieve items close to a query. It is common in enterprise RAG systems and semantic search.
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Chunking
Splitting documents into retrievable passages
Chunking splits a long document into units that can be indexed and retrieved. A passage that is too short loses context; one that is too long mixes topics and reduces precision.
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Top-k
The number of highest-ranked results a system keeps
Top-k indicates how many documents, passages or candidates move to the next stage. A small k raises the ranking bar; a large one adds coverage but also noise.
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Context window
The amount of information a model can process in one interaction
The context window limits the text, instructions, history and retrieved passages a model considers when answering. Available content may be truncated or compete for attention.
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Training cutoff
Approximate time boundary of training data
The training cutoff marks how far knowledge learned during training extends. It does not prevent a search-connected product from using later information.
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Real-time retrieval
Searching for information while generating the answer
Real-time retrieval allows current sources to be searched while the answer is generated. Latency, bot access and the provider’s index affect which pages appear.
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Knowledge graph
A network of structured entities and relationships
A knowledge graph represents people, brands, products, places and their relationships. It helps resolve ambiguity and connect facts from multiple sources.
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Entity
An identifiable object such as a brand, person, product or place
An entity is something a system can distinguish and connect to properties and other entities. Correct disambiguation prevents a brand from being confused with similar terms or companies.
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Entity salience
The relative importance of an entity within content
Entity salience expresses whether a brand or concept is central or merely incidental in a document. It is not the same as counting name occurrences.
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Multi-source RAG
RAG that combines evidence from multiple sources
Multi-source RAG retrieves and synthesizes information from different documents, domains or collections. It can improve coverage but also introduces conflicts and attribution issues.
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Passage retrieval
Selecting specific passages rather than full documents
Passage retrieval looks for the most relevant block within a page. This is why a well-ranked URL may still fail to provide the passage a generator needs.
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Machine readability
How easily a system can interpret content and data
Machine readability depends on semantic HTML, stable structure, clear data and explicit relationships. It does not mean writing for robots or sacrificing the human experience.
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Citations and sources
Terms for evaluating attribution, grounding, source selection and hallucinations.
Citation
A visible reference to a source within an answer
A citation links, identifies or attributes part of an answer to a source. It should be measured separately from a simple brand mention.
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Source attribution
Explicitly assigning a claim to its origin
Attribution indicates which source supports a specific claim. An answer may show links yet attribute a fact incorrectly or connect it to the wrong source.
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Citation grounding
The degree to which a citation truly supports generated text
Citation grounding evaluates whether linked evidence contains what is needed to support a claim. It is not enough for the source and answer to discuss the same topic.
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Citation correctness
The share or quality of citations that correctly support a claim
Citation correctness measures whether each reference is relevant and faithful to its content. It detects decorative, out-of-context or incorrectly matched citations.
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Citation completeness
The degree to which verifiable claims have support
Citation completeness checks how many claims needing evidence are accompanied by a source. An answer may have correct citations yet leave many facts unsupported.
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Source selection
The process of choosing which sources an engine uses or displays
Source selection happens after candidate retrieval and may depend on relevance, authority, freshness, diversity and usefulness for the answer.
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Source diversity
The variety of domains, formats and viewpoints used
Source diversity measures whether an answer depends on a few domains or combines independent viewpoints. More sources do not guarantee better evidence if they copy one another.
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Source freshness
The age or recency of a source selected by an engine
Source freshness compares a source’s publication or update date with query time. Its importance changes with intent and topic stability.
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Citation probability
An estimate of the likelihood that a source will be cited
Citation probability summarizes signals that make a URL more likely to move from candidate to visible evidence. It is an estimate, not a guarantee for any query.
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Citability
How easily a source can be used and cited
Citability combines clarity, extractability, evidence, attribution and query fit. Content can be good for people yet difficult to turn into a supportable passage.
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AI hallucination
Generated content that appears plausible but is unsupported
A hallucination occurs when a model invents or distorts facts, sources or relationships. For a brand it can affect prices, services, locations, people or reputation.
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Source concentration
The share of citations accumulated by a small group of domains
Source concentration shows whether a few sites dominate citations for a topic or engine. High concentration makes it harder for a new domain to enter.
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Content and authority
Signals that make a source interpretable, retrievable and useful in an answer.
Structured data
Data in an explicit format describing entities and properties
Structured data makes products, organizations, authors, dates and relationships easier to interpret. On the web it is commonly implemented with Schema.org vocabulary.
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Schema markup
Implementation of Schema.org vocabulary on a page
Schema markup describes in a structured way what type of entity or content appears at a URL. JSON-LD is common, but markup does not replace visible content.
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DefinedTerm
Schema.org type for representing a defined term
DefinedTerm declares the name, description and membership of a concept in a glossary or terminology set. It helps express the semantic role of a page like this one.
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llms.txt
A proposed file for pointing AI systems to useful content
llms.txt is a proposed convention for summarizing and linking useful content for models and agents. It is not a universal standard and does not guarantee indexing, retrieval or citations.
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Information gain
New value a source adds beyond what is already available
Information gain expresses how much useful, distinctive knowledge a page adds. Original data, methodology, examples and synthesis increase its potential value as a source.
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Content freshness
The material recency of published information
Content freshness depends on whether facts remain current, not merely on changing a date. It matters more for pricing, software, news and comparisons than for stable concepts.
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Topical authority
Perceived sustained expertise on a topic
Topical authority comes from covering a field with depth, consistency, experience and clear connections across content. It is not earned by publishing many shallow pages.
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Earned media
Coverage or mentions earned on third-party channels
Earned media includes news, reviews, analysis, communities and editorial references the brand does not directly control. It can provide corroboration and access to domains engines already cite.
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E-E-A-T
Experience, expertise, authoritativeness and trust
E-E-A-T is a Google-associated quality framework covering experience, expertise, authoritativeness and trust. In GEO it is a useful review lens for credentials and reliability, not a direct LLM score.
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Digital PR
Public relations focused on online coverage and authority
Digital PR creates assets, studies and stories that can earn editorial mentions and links. In GEO it expands the external sources that describe and validate an entity.
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Content extractability
How easily a useful passage can be isolated without losing meaning
Content extractability indicates whether a system can isolate an answer, datum or list while preserving subject, context and conditions. It depends on writing and structure, not only technical format.
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Direct answer format
A structure that answers early and expands afterward
Direct answer format places a clear definition or conclusion first, then develops nuance, evidence and exceptions. It aids extraction without reducing content to one sentence.
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Multimodal GEO
Optimization for systems combining text, image, audio or video
Multimodal GEO studies how systems understand and rank content made of several modalities. Consistency among image, text, metadata and landing page becomes more important.
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Vision-language model
VLM
A vision-language model processes images and text together to describe, compare, search or rank content. Some GEO studies examine how these models rerank products and results.
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Internal linking
The network of links among pages on one site
Internal linking helps discover content, distribute context and express topical relationships. In a glossary it connects each term to the set and to neighboring concepts.
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Crawlability
A bot’s ability to access and navigate a site
Crawlability depends on HTTP responses, robots rules, links, rendering and technical stability. An inaccessible page cannot enter many retrieval processes.
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Visibility measurement
Metrics for comparing brands, engines, prompts, sources and changes over time.
AI visibility
The measurable presence of an entity in AI answers
AI visibility combines whether a brand appears, how often, in what position, with what sentiment and which sources accompany it. It should be defined before comparing tools.
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Brand mention
An explicit or recognizable appearance of a brand
A brand mention occurs when an answer names or identifies the company, product or variant. It may exist without a link and may be positive, neutral or negative.
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Recommendation rate
The percentage of answers in which a brand is recommended
Recommendation rate counts answers where the brand appears as an advised option, not every mention. It requires classifying the brand’s semantic role in the answer.
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Mention role
The semantic function a brand serves in an answer
Mention role distinguishes recommendation, alternative, comparison, warning, example or incidental reference. It provides more information than a binary count.
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Mention rate
The percentage of answers in which an entity appears
Mention rate divides answers containing the brand by total valid runs. Technical errors should be excluded and results segmented by prompt and engine.
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Citation rate
The percentage of answers that cite a source or domain
Citation rate measures how often a URL, domain or brand appears as a visible source. Its denominator may be answers, prompts or runs and must be stated.
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AI share of voice
A brand’s share of presence versus competitors
AI share of voice compares a brand’s presence with total mentions or visibility across a competitive set. The formula may weight position, engine or role.
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Brand sentiment
The tone or evaluation used by AI to describe a brand
Sentiment classifies how an entity is treated as positive, neutral, mixed or negative. Context and role matter more than isolated words.
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Visibility score
A composite index summarizing several presence signals
A visibility score combines metrics such as mentions, position, recommendation or engine coverage. There is no universal formula, so two platforms may produce different values.
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Average position in AI
The average order in which a brand appears within answers
Average position summarizes where a brand appears in lists or recommendations. It requires rules for non-list answers, repeated mentions and absent brands.
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Prompt monitoring
Repeated execution of queries to measure change
Prompt monitoring repeats a stable set of questions across defined engines and contexts. It reveals mentions, citations, sentiment and volatility.
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Response volatility
Variation in results across equivalent runs
Response volatility reflects that the same prompt can produce different brands, positions or sources. It depends on sampling, the index, personalization and engine changes.
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Organic search overlap
Overlap between AI-cited sources and organic search results
Organic overlap compares domains or URLs in generative answers with traditional results for the same query. Low overlap indicates different source-selection ecosystems.
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Discovery gap
The gap between name recognition and discovery
Discovery gap describes brands an AI recognizes when named but does not recommend in generic queries. It separates brand knowledge from competitive visibility.
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Exposure bias
Unequal distribution of visibility among entities or sources
Exposure bias occurs when already prominent brands, creators or domains systematically receive more visibility. It can reinforce existing hierarchies.
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Research metrics
Measures used in research to quantify ranking, stability, overlap and risk.
NDCG
Normalized Discounted Cumulative Gain
NDCG evaluates a ranking by considering relevance and giving more weight to higher positions. It is normalized so queries with different candidate sets can be compared.
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Jaccard similarity
A measure of overlap between two sets
Jaccard divides shared elements by the union of two sets. In GEO it can compare cited domains, recommended brands or results across runs.
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Kendall tau
Rank correlation between two ordered lists
Kendall tau measures how many pairs preserve the same order across two rankings. In GEO research it helps study the stability of positions or influence.
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Promotion success rate
How often an optimization moves an item to a target rank
Promotion success rate measures whether a document or product reaches a threshold such as top 5, top 3 or first place after an intervention.
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Worst-case performance
The lowest result observed across queries or scenarios
Worst-case performance evaluates the negative tail rather than only the average. In multi-query GEO it reveals whether an edit improves some questions but seriously harms others.
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Downside risk
The magnitude or frequency of losses versus a baseline
Downside risk focuses evaluation on degradation rather than positive variation. It shows how much harmed queries worsen after a change.
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Win-tie rate
The share of cases that improve or do not worsen
Win-tie rate counts queries where an intervention wins or ties against a baseline. It complements average improvement with a consistency measure.
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Risk and manipulation
Practices and attacks that can degrade quality or manipulate a generative answer.
Prompt injection
Malicious instructions inserted into content or inputs
Prompt injection tries to alter model behavior through instructions the system should not follow. It may appear in retrieved pages, documents or user messages.
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Content poisoning
Publishing or altering information to contaminate answers
Content poisoning introduces false or manipulated information into sources a model may retrieve or learn from. It aims to alter facts, recommendations or reputation.
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Adversarial GEO
Techniques designed to manipulate ranking or generation
Adversarial GEO studies deceptive changes or attacks that exploit retrievers, rerankers or multimodal models to gain unearned exposure.
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Keyword stuffing
Artificial repetition of words to influence retrieval or ranking
Keyword stuffing inserts terms excessively or unnaturally to manipulate relevance. It can reduce passage clarity, trust and usefulness.
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Source manipulation
An attempt to alter which sources a system retrieves, selects or cites
Source manipulation tries to force selection or attribution through deceptive signals, artificial networks or content designed to exploit the pipeline.
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