An AEO glossary helps marketers make sense of the terms used to describe visibility in AI answers and traditional search. Here are 50 terms, explained in plain English, with an example of how each one applies to marketing.
The terminology is still developing, and platforms do not all work the same way. Treat these definitions as a practical shared vocabulary, not as a promise that every answer engine uses an identical system. For a wider view of the work, explore SearchFIT’s AI search resources and its AI search visibility articles. If you want to check your current presence, start with the free AI search audit.
How to use this AEO glossary
Use the glossary to align marketing, SEO, content and analytics teams. For example, agree what your organisation means by a citation, a brand mention and a recommendation before comparing results across campaigns. Those measures describe different kinds of visibility.
The technical terms can help explain why content appears, or does not appear, in an AI answer. They are not all direct ranking factors, and the exact retrieval and generation process is often platform-specific. A practical strategy is to make useful information easy to crawl, understand, verify and quote, then measure what happens across a consistent set of questions.
The terms below are in alphabetical order. Each definition includes an example to connect the concept to everyday marketing work.
AI crawler or scraper
An AI crawler or scraper is a bot that visits web pages to collect information. Depending on the operator and purpose, it may gather material for model training, search indexing or retrieval that supports a live answer. These are different uses, so a crawler’s presence alone does not tell you exactly how a page will be used.
Example: A technical SEO specialist checks server logs to see whether an AI-related bot can fetch a product guide. The team also checks the site’s bot rules and page access settings before assuming the guide is available to every answer engine.
AI SEO
AI SEO is a broad label for work that helps a brand get found and represented across search experiences that use AI. It can include conventional SEO, answer engine optimisation, generative engine optimisation, technical access, content quality and measurement. The phrase is used in different ways, so explain what work it covers when reporting it.
Example: An agency’s AI SEO plan could include improving category pages for Google, answering common buyer questions clearly, checking brand descriptions in AI responses and tracking source citations over time.
AI share of voice (AI SOV)
AI share of voice is a comparison of a brand’s visibility against a defined group of competitors across selected AI search prompts. The calculation might include mentions, citations or recommendations. State which signals and prompts you include, because changing the definition can change the result.
Example: A SaaS team tracks 40 prompts about project management tools. It reports the percentage of those prompts where its brand appears, then reports citation share separately so readers can distinguish being named from being linked as a source.
Answer engine
An answer engine is a search product that responds to a question with a direct answer, a summary or a conversation, rather than only presenting a list of links. Some answer engines include citations or links to supporting pages. Others may combine generated text with familiar search results.
Example: A buyer asks an answer engine to compare accounting software for a small business. The product summarises options, may cite source pages and can invite follow-up questions. A traditional search results page, by contrast, typically makes the user open and compare several pages themselves.
Answer engine optimisation (AEO)
Answer engine optimisation, or AEO, is the practice of making useful, accurate information easier for answer engines to find and use in a response. It includes clear answers, descriptive page structure, reliable sources and accessible technical implementation. AEO does not guarantee that a particular page or brand will be selected.
Example: A retailer adds a clear, current returns answer to its policy page, uses headings that match customer questions and makes sure the policy can be reached by search crawlers. That improves the page’s usefulness to people and its potential to support an answer.
Answer extraction
Answer extraction is the process of identifying a short, relevant passage from a longer page that can help answer a question. A passage may be useful on its own, but the surrounding page still matters for context and trust. Concise writing can help, provided it does not remove important qualifications.
Example: A page explains how long delivery takes in a clearly labelled section. The answer gives the standard delivery window and notes that remote areas may take longer, rather than offering a vague promise that could be misleading if quoted alone.
Answer-first content
Answer-first content puts the direct response near the beginning of a section, then adds explanation, evidence and useful detail. It respects readers who want a quick answer without reducing a complex subject to an oversimplified claim. The opening should answer the heading’s question, not merely repeat it.
Example: A page headed “What is citation share?” begins by defining the measure in one sentence. It then explains the formula, what counts as a citation and how the team uses the metric. This structure is easier to scan than burying the definition at the end.
Brand authority
Brand authority is the degree to which people and information sources recognise a brand as knowledgeable and dependable in a subject area. It is not one universal score or a single setting that marketers can switch on. Useful expertise, a consistent identity, credible evidence and independent references can all contribute to how a brand is understood.
Example: A cybersecurity company publishes detailed guidance reviewed by qualified specialists, keeps product documentation current and earns relevant industry references. Those actions give buyers and information systems more substance to assess than a series of unsupported claims that the company is a leader.
Brand mention rate
Brand mention rate measures how often a brand is named in answers to a defined set of prompts. A mention does not necessarily include a link, an endorsement or a positive description. Track sentiment and citations separately if those distinctions matter to the business question.
Example: A marketing team tests prompts such as “Which tools help manage online reviews?” and records whether its product is named. It labels a neutral description as a mention, not automatically as a recommendation, and records a source link in a separate field.
Brand memory or parametric memory
Brand memory, sometimes called parametric memory, refers to information a model may have learned during training and retained in its internal parameters. It is different from information retrieved from a live webpage during a search. A model’s internal knowledge can be incomplete or out of date, so it should not be treated as an authoritative brand record.
Example: A model may describe a company using information from an older product launch. A marketer compares that response with a search-grounded answer that links to the current product page, then checks whether the official site clearly explains what has changed.
Brand sentiment drift
Brand sentiment drift is a change over time in the way AI answers describe or evaluate a brand. The wording may shift from positive to neutral, or the answer may start associating the company with a different market position. A few variable outputs do not prove a lasting trend, so use repeatable prompts and review examples.
Example: A software company notices that monitored answers increasingly describe it as suited to large enterprises rather than small teams. It checks whether product messaging, third-party descriptions or recent changes could explain the shift before changing its positioning.
Citation panel or source rail
A citation panel, sometimes called a source rail, is an interface area that displays sources used or suggested alongside an AI answer. It may appear as a list, sidebar, carousel or set of links. A page shown there is not necessarily quoted in the answer, so distinguish panel inclusion from an inline citation.
Example: In a research report, an analyst records that a brand’s product guide appeared in the source panel, while a separate column notes whether the answer linked to that guide beside a specific claim.
Citation share
Citation share is the proportion of observed citations within a defined prompt set that belong to a particular brand or domain. A clear calculation needs a scope: which prompts, answer engines, dates and citation types count? It measures source representation, not market share or the share of people who saw an answer.
Example: A team reviews 100 citations across a fixed set of category prompts and finds that 12 point to its domain. It reports a 12% citation share for that sample, names the engines and period, and avoids presenting the figure as a universal measure of visibility.
Context window
A context window is the amount of text a model can consider at one time while processing an input and producing a response. It can include the user’s prompt, earlier conversation, retrieved passages and instructions. The limit and handling vary by model, and a larger context window does not mean every supplied detail will receive equal attention.
Example: An answer engine retrieves several pages to answer a detailed comparison question. A concise page with clear sections may make its key facts easier to identify than a long page where important details are scattered across unrelated material.
Conversational search
Conversational search lets a person search through a back-and-forth exchange. The system may use previous turns to interpret a follow-up, so the user does not need to repeat every detail. This can make questions more natural, but it also means the same words may be interpreted differently depending on conversation history.
Example: A buyer asks for project management tools, then follows up with “Which works best for a remote team?” The second question depends on the first. A marketer testing visibility should record the full exchange, not just the final sentence.
Corroboration score
A corroboration score is a proposed or tool-specific measure of how consistently a fact appears across multiple sources. There is no single universal formula. Sources may disagree, repeat one another or rely on the same original information, so a higher count alone does not prove a claim is correct.
Example: A company checks whether its product name, headquarters and current pricing match across its website and trusted directories. It treats mismatches as a reason to investigate and correct the record, rather than assuming that the most common version is automatically accurate.
Crawlability
Crawlability describes whether automated crawlers can access and follow a website’s pages. Robots rules, server errors, broken links and access controls can make important content harder to discover. Crawlability is a technical starting point, not a guarantee that a page will be indexed, retrieved or cited.
Example: A retailer checks that its product category pages return successfully, can be reached through internal links and are not blocked by an accidental robots rule. It then checks indexing separately, because access and inclusion in a search index are different things.
Dense retrieval
Dense retrieval finds potentially relevant information using numerical representations of meaning, often called vectors. It can match a question to text that uses different words but expresses a related concept. Dense retrieval is one approach used in some search and retrieval systems, not a description of every answer engine’s complete process.
Example: A user asks for “ways to reduce abandoned carts”, while a page discusses “checkout completion improvements”. A dense retrieval system may recognise the topical connection even though the exact phrases do not match.
Entity-based SEO
Entity-based SEO organises information around identifiable things, such as a business, person, product, place or concept, rather than relying only on repeated keyword phrases. It aims to make relationships and facts clear. The approach still depends on accurate content and cannot force a search system to recognise an entity in a particular way.
Example: A software company clearly distinguishes its brand, its product names and its parent company across its site. Product pages, author biographies and company details use consistent names so readers and search systems are less likely to confuse related entities.
Entity resolution
Entity resolution is the process of deciding whether different names or records refer to the same real-world thing. A company might appear under an abbreviated name, a legal name and a domain name. Consistent information helps reduce ambiguity, although an answer engine’s interpretation cannot be controlled completely by a brand.
Example: A business uses “Northstar Analytics” publicly but appears in an older directory as “North Star Data Pty Ltd”. It updates the directory and links its company information clearly, helping customers understand that the records describe the same business.
Featured snippet
A featured snippet is a highlighted excerpt that Google may display near the top of some search results to answer a query. It can take the form of a paragraph, list or table. It is a traditional search feature, not a synonym for an AI answer or a guarantee of a website visit.
Example: A page that explains how to calculate customer churn in a concise, well-labelled section may be considered for a snippet. The team still checks whether the page earns that placement and whether users continue to visit for the full explanation.
Query fan-out
Query fan-out is the process in which a search system expands one question into related searches or sub-questions. The extra searches can help gather information from different angles before an answer is composed. How fan-out works and whether a platform uses the term publicly varies. Read Surfer’s explanation of query fan-out for a related overview.
Example: A user asks, “What is the best CRM for a growing Australian SaaS company?” Related searches might cover pricing, integrations, company size and local support. A useful content plan answers those underlying buyer questions instead of repeating only the phrase “best CRM”.
Generative engine optimisation (GEO)
Generative engine optimisation, or GEO, is the practice of improving the chance that content and brand information will be used, cited or represented in answers generated by AI systems. It overlaps with AEO and SEO, but its emphasis is often on multi-source synthesis and brand representation. The terminology is still developing.
Example: A B2B team makes a comparison guide more useful by adding clear criteria, evidence and original detail. It also checks whether the guide is accessible to search systems and monitors if relevant answer engines cite it. The GEO research paper studies ways to improve source visibility in generative engine responses.
GEO-bench
GEO-bench is the benchmark described in the GEO research paper, used to evaluate optimisation approaches across a set of queries and generative engines. A benchmark is a controlled test, not a promise of the same results for every industry or live platform. Its findings need to be read in the context of its methodology.
Example: A marketer refers to the study when discussing why evidence and clear writing may matter, but does not tell a client that a tactic will produce the same percentage improvement for their own pages. The original GEO paper explains the research and its limits.
Grounding
Grounding is the use of external information to support an AI-generated answer. A system may retrieve pages, records or other material and use them as context. Grounding can make an answer more tied to available evidence, but it does not guarantee that the sources are correct or that the generated summary represents them accurately.
Example: An answer engine consults a company’s pricing page and refers to the listed plan. A marketer checks whether the answer reflects the conditions and date on that page, rather than assuming that a linked source automatically makes every sentence accurate.
Hallucination
A hallucination is a plausible-sounding AI output that is false, unsupported or not present in the information the system was given. It can involve invented facts, incorrect combinations of real details or a misreading of a source. Clear, current information can help, but no content format can eliminate hallucinations entirely.
Example: An AI answer says a software plan includes a feature that the company does not offer. The product team verifies the claim against its current documentation, corrects confusing public descriptions and reports the error without treating one response as proof of a widespread issue.
Indexability
Indexability is whether a search engine can include a page in its index after discovering and processing it. A page can be crawlable but still be excluded because of technical directives, duplicate content or other search-system decisions. Being indexed does not guarantee a high position or use in an AI answer.
Example: A team finds that an important guide is accessible to crawlers but has a noindex instruction. It removes the directive if it was accidental, then checks the page’s index status. That is a different check from confirming that the page loads in a browser.
Information gain
Information gain is the useful new knowledge a page adds beyond what a reader can already find in similar material. It may come from original research, a practical demonstration, a clear comparison or a specific expert explanation. Rewriting common advice with different wording does not necessarily add meaningful information.
Example: Several articles list generic email marketing tips. A new guide adds a documented test method, explains the audience and limitations, and shares a reusable checklist. That gives readers a reason to consult the page rather than another summary of familiar advice.
Inline citation
An inline citation is a link or source marker placed within an answer, often beside the statement it supports. It differs from a general sources list because it can connect a particular claim to a particular page. The presence of a citation does not necessarily mean the page was interpreted correctly.
Example: An AI answer states that a product supports a particular integration and places a link directly beside the claim. A monitoring team records the linked URL, checks whether it supports the statement and notes whether the answer also mentions competing products.
Knowledge graph
A knowledge graph is a structured way of representing entities and the relationships between them. It can connect people, organisations, products and concepts as linked facts. Search engines may use knowledge graphs or similar systems, but marketers should avoid assuming that adding a particular field automatically creates or controls a graph entry.
Example: A company website identifies the business, lists its products and explains which product belongs to which product family. That consistent information helps people and systems distinguish the organisation from another business with a similar name.
llms.txt
llms.txt is a proposed website file intended to give language-model tools a curated, readable guide to useful site content. It is not a universal requirement for AI visibility, and support can differ between systems. Do not rely on it instead of accessible pages, good site structure or ordinary technical SEO checks.
Example: A documentation team creates a simple index of its most useful product guides, then tests whether its intended tools use the file. The team continues to maintain the actual documentation pages because a directory file cannot replace complete, accurate source content.
Machine-readable data
Machine-readable data is information presented in a structured format that software can interpret more consistently than unstructured prose alone. Examples include structured data markup, product feeds and clearly labelled tables. Structured data can clarify facts, but it does not guarantee a rich result, citation or particular AI response.
Example: An online store keeps product names, prices and availability consistent between its product pages and product feed. It checks that the structured details match what a customer can actually see, rather than adding fields that make claims the page does not support.
Match protocol or match layer
Match protocol or match layer is an informal term for how a brand’s information, subject relevance and technical accessibility align with what a search system retrieves for a question. It is not a universally standardised ranking mechanism. Use the phrase only after explaining what a particular tool or team means by it.
Example: An agency uses “match layer” as shorthand for reviewing whether a client’s page answers a prompt, whether the brand is clearly identified and whether the page can be accessed. It records those as separate checks, not as one mysterious score.
Model drift
Model drift is a change in a model or answer engine’s behaviour over time. The change could affect how it interprets prompts, retrieves sources or writes answers. A new response does not by itself show that the underlying model has changed; other causes include fresh web content, product updates and normal output variation.
Example: A company sees fewer citations in a monthly prompt sample. Before blaming model drift, it checks that the prompts, locations, account settings and tracking method are consistent, and reviews whether its own pages or the competitors’ pages have changed.
Neural reranking
Neural reranking is a step in some search pipelines where a model reorders an initial set of candidate results by estimated relevance. A page can be retrieved at one stage and then placed lower or left out later. Reranking is a technical concept, and the precise stages vary between retrieval systems.
Example: A retrieval system first finds several guides about email deliverability. A reranker may place the guide that most closely answers the user’s question ahead of a broader marketing article, even if both contain related keywords.
Passage retrieval
Passage retrieval is the selection of a relevant section within a larger document, rather than treating the whole page as one indivisible result. It can help a system locate a specific answer in a long guide. Strong page organisation helps human readers too, but no heading structure guarantees that a passage will be retrieved.
Example: A long ecommerce guide has separate, descriptive sections for shipping, returns and payment methods. A question about return windows can be matched to the returns section without requiring the entire guide to be equally relevant.
Prompt panel
A prompt panel is a repeatable set of questions used to monitor AI search answers over time. It creates a consistent sample for comparing brand mentions, citations and recommendations. It is not necessarily a representative sample of all real customer questions, so teams should explain how the prompts were chosen and review them periodically.
Example: An agency builds a prompt panel around the problems its client solves, including comparison and buying questions. It keeps a stable core for trend reporting and separately tests new questions when products, competitors or customer language change.
Prompt stability
Prompt stability describes how consistently a system responds when it receives the same prompt on different occasions. Outputs may vary because of model updates, retrieved pages, settings or natural generation variation. Stability is a measurement question, not a promise that a prompt will always produce identical wording or sources.
Example: A researcher runs the same question more than once under documented conditions. The report notes changes in the answer and citations, rather than treating a single run as a permanent view of the brand’s visibility.
Query decomposition
Query decomposition is the breaking of a complex question into smaller questions or components that can be handled separately. It is related to query fan-out, though the terms do not always describe exactly the same system step. Decomposition can help answer a request with several conditions or topics.
Example: “Compare email platforms for a charity with a small team” can be decomposed into platform features, price, ease of use and suitability for charities. A content plan that covers those decision factors may serve the query better than one page built around a generic software keyword.
Recommendation rate or share of recommendation
Recommendation rate measures how often a brand is explicitly recommended in a defined set of relevant prompts. It differs from brand mention rate: a system may name a company without endorsing it. A useful report defines what counts as a recommendation and includes the prompt set, platform and time period.
Example: A team asks answer engines for product recommendations across a set of category questions. It records a recommendation only when the answer presents the brand as a suggested choice, not when it merely lists the brand while discussing the market.
Retrieval-augmented generation (RAG)
Retrieval-augmented generation, or RAG, is an approach in which a model retrieves external information and uses it as context when generating a response. The retrieval source may be a search index or another knowledge collection. RAG can connect answers to current material, but it does not ensure that retrieval or generation will be error-free.
Example: A support assistant searches a company’s help centre before answering a question about account settings. The assistant uses the retrieved article to form a response, while the support team still checks that the article is current and the answer preserves any important conditions.
Search intent
Search intent is the need or goal behind a person’s query. A person might want to learn, compare options, complete a task or buy something. The wording alone may not reveal the whole intent, so marketers should consider the context and likely next step rather than matching a phrase mechanically.
Example: Someone searching “best accounting software for contractors” is likely comparing options, while “how to reconcile a bank feed” suggests a practical task. A comparison page and a step-by-step help article serve different intents even if both relate to accounting software.
Semantic chunking
Semantic chunking divides content into passages that each cover a coherent idea. The goal is not to make every paragraph extremely short, but to keep sections focused enough that readers and retrieval systems can understand their subject. Useful headings and logical transitions make the relationship between chunks clearer.
Example: A guide about choosing a CRM has separate sections for contact management, reporting, integrations and pricing. Each section explains one topic and links it to the buying decision, instead of mixing every feature into a single dense block of text.
Source diversity index
A source diversity index is a tool-specific way to describe how varied the sources are in a set of AI answers. It might consider the number or types of independent publishers cited. There is no universal formula, and a long list of similar sites does not necessarily mean a topic has broad or balanced evidence.
Example: A team reviews the sources used across answers about a medical product. It distinguishes official product information, independent clinical sources and discussion forums, rather than counting every URL as an equally independent and trustworthy reference.
Sparse retrieval
Sparse retrieval finds matches using explicit terms and their occurrence in documents. Traditional lexical search methods, including BM25, are examples of approaches in this family. Sparse retrieval can work well when wording is specific, while it may miss relevant passages that express the same idea with different vocabulary.
Example: A user searches for “Shopify returns policy”. A page using those exact terms may be easy for lexical search to match. Clear synonyms and descriptive page language can also help a wider set of readers understand the page’s subject.
Synthetic queries
Synthetic queries are search questions generated by a system rather than typed directly by a person. They may be used to explore aspects of a broader request, such as features, pricing or alternatives. Their presence and role depend on the search system; marketers usually cannot see every internal query that may have been used.
Example: A system answering “Which payroll tool suits a hospitality group?” may look for information about shift work, multiple locations and payroll integrations. A marketer can plan content around these real decision factors without claiming to know the exact hidden searches.
Temporal decay in AI search
Temporal decay describes the possibility that older information becomes less useful or less likely to be selected as facts, products or sources change. It is not a fixed expiry rule. Some evergreen pages remain useful for years, while pricing or policy pages can become misleading much sooner if they are not maintained.
Example: A software company reviews its integrations guide after a partner changes its API. The team updates the page and its publication details, then checks that related product documentation also agrees, rather than changing the date without updating the content.
Topical authority
Topical authority is a descriptive term for the depth and credibility of a site’s coverage of a subject. It is not a single public score. A strong body of useful, accurate material can help readers understand a company’s expertise, especially when individual pages support one another without repeating the same thin advice.
Example: An ecommerce platform publishes practical guides on inventory, checkout, returns and product data. Each guide addresses a distinct problem and points to relevant documentation, giving merchants a coherent resource rather than a collection of unrelated keyword pages.
User-generated content (UGC)
User-generated content is material created by customers or members of a community, such as reviews, forum posts, product questions and discussion threads. It can reveal how people describe a problem or experience. It can also be inaccurate or unrepresentative, so marketers should not treat every post as verified evidence.
Example: A retailer reviews customer questions to find recurring confusion about product sizing. It updates its own size guide with verified measurements, while keeping customer opinions distinct from the company’s official product specifications.
Vector embeddings
Vector embeddings are numerical representations of text or other data that capture patterns of similarity. In a vector search system, items with related meanings may be found near one another in a mathematical space. Embeddings help explain one type of semantic search, but do not describe every way an AI system finds information.
Example: A help article about “resetting a password” may be considered related to a question about “getting back into my account”, even when the wording differs. The article still needs to contain a correct answer for the retrieval to be useful.
Zero-click search
Zero-click search is a search journey in which someone gets the information they need without visiting an external website. A direct answer, calculator, map or AI response may satisfy the query on the search or chat interface. A zero-click result can reduce visits, but a brand may still gain awareness or influence.
Example: A person asks how many millilitres are in a litre and sees the answer immediately. For a complex buying decision, however, the person may still need a detailed comparison or product page. The Gartner forecast projected a decline in traditional search volume as AI chatbots and virtual agents grow; it was a forecast, not a guaranteed outcome for every site.
Putting the terms into practice
The terms matter most when they change what your team does. A practical starting point is to pick a manageable set of buyer questions, then review both the content on your site and the answers shown by relevant platforms.
Use this sequence:
- Choose a question set. Include discovery, comparison and task-based questions that reflect how customers actually talk about their needs. Keep a stable set for reporting, and note when you add or remove prompts.
- Check the answer. Record whether your brand is mentioned, recommended or cited. Capture the source URL and the wording around it. Those are separate observations, not interchangeable measures.
- Review the source page. Confirm the page answers the question, is current, can be accessed and supports the claim that appears in the answer. Correct contradictions between your site and other important public information.
- Improve the useful detail. Add missing explanations, evidence or decision criteria. Use clear headings and focused sections, but write for the person who needs the information rather than trying to game a hypothetical system.
- Measure again. Repeat the same checks under similar conditions. Report the sample, time period and method so stakeholders can interpret changes without assuming that every variation reflects a lasting trend.
Research on generative engine optimisation offers one reason to test presentation and evidence rather than relying on keyword repetition alone. The Princeton GEO study evaluated content strategies in a research setting. Its results are useful evidence to consider, not a guarantee that any individual tactic will produce the same effect on a live platform.
If you are planning work across a larger site, SearchFIT’s AI search visibility guidance can help turn the terminology into a practical programme. Start with clear definitions, reliable pages and a measurement method your team can repeat.