Glossary. AI Search
terms defined.

Agentic AI refers to artificial intelligence systems that can proactively take actions and perform tasks on behalf of a user, going beyond simply providing information. In the context of search, agent...

Agentic Capabilities refer to AI systems that act autonomously, completing tasks like booking, purchasing, or form submission on behalf of the user. This goes beyond providing information, enabling ac...

AI Attribution Rate measures the frequency with which a brand or website is explicitly named, cited, or referenced in AI-generated answers. In the context of AI search, where zero-click interactions a...

AI Bots, such as OpenAI's GPTBot, Google's Google-Extended, and Anthropic's ClaudeBot, are specialized web crawlers designed to gather data for training and powering Large Language Models (LLMs). Unli...

AI Citation Count refers to the total number of times a specific piece of content or a website is referenced or cited across various Large Language Models (LLMs) and AI search platforms (e.g., ChatGPT...

AI Engineering is the discipline of designing, developing, and deploying scalable, reliable AI systems using engineering best practices. It integrates MLOps, data engineering, and ethical AI framework...

AI Mode refers to an interface where users receive contextual, conversational answers, rather than traditional link lists. AI Modes use LLMs to provide summaries, actionables, or multi-step answers di...

AI Model Crawl Success Rate measures how much of a website's content AI bots (such as GPTBot, Google-Extended, or ClaudeBot) are able to successfully access and crawl. Similar to traditional SEO's cra...

AI Overviews, also known as Search Generative Experience (SGE) in Google's context, are features in search engines where AI-generated summaries and answers are displayed directly at the top of the sea...

AI Search Optimization refers to the process of optimizing content and digital strategies for search engines powered by artificial intelligence, such as those utilizing Large Language Models (LLMs) an...

Anchor links (table of contents, section IDs) make it easier for engines to reference exact sections. They also improve user trust when citations jump to the relevant passage.

Answer Engine Optimization (AEO) emerged with the rise of featured snippets and knowledge panels in search engines. Its objective is to optimize content so that search engines can directly answer user...

Answer grounding means tying an AI-generated response to verifiable sources, often via retrieval. Grounded answers cite or link to supporting documents, improving trust, auditability, and compliance, ...

Answer provenance documents where each part of an AI response came from. Clear provenance (citations, quotes, metadata) builds trust, supports audits, and helps diagnose errors or bias.

Key fields include headline, author, datePublished/dateModified, description, and mainEntityOfPage. Complete article markup improves content parsing and potential citation.

Author pages showcasing qualifications, affiliations, and publications reinforce E-E-A-T. They help AI systems attribute expertise, especially for sensitive topics where human oversight is valued.

An Autonomous Lexicon Engine (ALE) is a self-directed language system that generates, organizes, and optimizes new linguistic units, such as terms or metadata clusters, based on external signals like ...

Bi-encoders encode queries and documents independently into embeddings, enabling fast vector similarity search, while cross-encoders jointly encode the query and document to compute a more accurate re...

Okapi BM25 is a probabilistic ranking function used in traditional search engines. It scores how well a document matches a query by considering term frequency (how often the query terms appear) and in...

Brand Mentions are a critical metric in AI search because they represent a form of visibility and authority in a zero-click environment. When an AI model mentions a brand in its response, it is effect...

BreadcrumbList marks navigational hierarchy. It clarifies page context and relationships, aiding entity understanding and more accurate retrieval.

Canonical URLs signal the preferred version of a page when duplicates exist. Proper canonicalization consolidates signals and prevents fragmented embeddings across near-duplicate pages, improving retr...

CCBot is Common Crawl’s crawler. Many AI models leverage Common Crawl datasets as part of their pretraining, so allowing CCBot helps your content be represented in broad web corpora.

Publishing change logs and last-modified dates signals recency and transparency. It also helps AI systems identify updated chunks worth reprocessing and citing.

Overlap repeats a small portion of text between consecutive chunks to preserve context for boundary-spanning facts. It improves retrieval of details that sit near chunk edges.

Chunk Retrieval Frequency is a Key Performance Indicator (KPI) in AI search that measures how often a modular content block (or 'chunk') from a website is retrieved by an AI model in response to user ...

Chunk size balances context completeness and precision. Typical ranges are 200–400 words or token-based windows with 10–20% overlap; test against retrieval and faithfulness metrics.

Chunkability refers to how easily a piece of content can be broken down into smaller, coherent, and self-contained blocks of information, or 'chunks'. AI models, particularly those using Retrieval-Aug...

Citation Drift refers to the phenomenon where the sources cited by AI search tools change significantly over time, even for identical questions. Unlike traditional search results which are relatively ...

Citation-First Search describes AI-generated responses that include explicit source references, such as footnotes or linked citations, in their answers. This enhances transparency, trust, and enables ...

Cited Domain Share measures the percentage of AI citations attributable to specific domains in your niche. Tracking shifts helps you benchmark authority and set GEO targets.

Claude-Web and ClaudeBot are Anthropic’s web access agents used to fetch content for browsing and retrieval features. Allowing access helps Claude models ground answers with up-to-date sources.

ColBERT is an efficient neural retrieval model that uses late interaction to balance accuracy and speed. It’s relevant to AI search teams exploring advanced semantic retrieval beyond standard embeddin...

Comparison pages with structured features, pros/cons, and pricing help AI compose recommendations. They’re frequently cited for ‘best of’ and ‘X vs Y’ prompts across engines.

Adopt a regular refresh cadence tied to topic volatility. High-change domains (AI, finance, security) benefit from monthly or even weekly updates to align with freshness-weighted rerankers.

The context window is the maximum amount of text (measured in tokens) an LLM can consider at once when generating an answer. Longer context windows allow models to incorporate more retrieved chunks, i...

Core Web Vitals (LCP, INP, CLS) primarily affect user experience and classic SEO, but fast, stable pages also help AI crawlers and reduce rendering failures. Moreover, performance aligns with SSR/SSG ...

Data poisoning is the deliberate insertion of misleading or harmful data into sources that AI models train on or retrieve from. Poisoned data can skew answers or harm brand perception. Monitoring cita...

Publishing datasets and transparent benchmarks creates evidence-heavy assets that AI engines cite as proofs. They contribute to Machine-Validated Authority and attract external references.

Yes. Exposing publication and modification dates via visible UI and schema helps freshness-sensitive rerankers and informs users of recency.

Well-structured API references, code samples, and troubleshooting guides are prime retrieval targets for AI assistants. They answer specific ‘how do I…’ prompts and earn durable citations.

Disambiguation resolves confusion between entities with similar names (e.g., brands, products, people). Explicit entity definitions, context, and schema reduce mix-ups and improve retrieval precision.

E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trustworthiness. It is a set of guidelines used by human quality raters for Google Search and is increasingly crucial for AI Search. AI...

Edge caching stores content closer to users and bots, reducing latency and improving reliability for crawlers. It also helps ensure timely access to updated pages, reinforcing freshness signals.

Embedding Relevance Score is a metric that quantifies the semantic similarity between a user's query and the content's embeddings. A higher score indicates a stronger alignment between the query's int...

Embeddings are numerical representations of text, images, or other data that capture their semantic meaning and relationships. In AI search, both user queries and content are converted into these nume...

Entity Clarity refers to the unambiguous and consistent representation of named entities (such as people, organizations, products, or concepts) within a piece of content and across the web. For AI mod...

Entity linking associates mentions in text with canonical entries in a knowledge base (e.g., linking ‘Apple’ to Apple Inc.). Correct linking enhances machine understanding and retrieval alignment, esp...

Ethical AI refers to the development and deployment of AI systems in ways that prioritize fairness, transparency, privacy, and accountability. It involves bias mitigation, data protection, and human o...

Evaluation measures in IR are metrics used to assess how effectively a system retrieves relevant content. Common measures include precision (exactness of results), recall (completeness), F1-score, pre...

Explainable AI (XAI) focuses on making AI system decisions transparent and interpretable. It enables understanding of how a model arrives at its outputs, crucial for trust, compliance, and debugging, ...

Third-party validations (press, awards, peer reviews) signal real-world credibility that AI systems value. Diversified corroboration reduces reliance on your own site alone for authority.

FAQPage schema marks up question-answer pairs on a page. It aligns well with AI search needs by exposing clear Q&A chunks that are highly retrievable and directly usable in synthesized answers.

A Freshness Scoring Profile is a component of an AI search model's ranking system that prioritizes recent content over older, potentially more authoritative content. For example, ChatGPT has been foun...

Generative AI refers to AI models capable of creating new content, such as text, images, or audio, by learning patterns from existing data. Examples include GPT, DALL·E, and other models that generate...

Generative Engine Optimization (GEO) is a term used to describe the optimization of content for AI-driven search tools like Google's Search Generative Experience (SGE), Bing Chat, and ChatGPT. It focu...

Google-Extended is a control that lets site owners manage whether content is used to improve Google’s AI models. Allowing it can increase your inclusion in AI Overviews; disabling reduces training usa...

GPTBot is OpenAI’s crawler used to fetch publicly available content for model training and to power retrieval features. Allowing GPTBot increases the chance your content informs ChatGPT answers and ci...

Hallucinated URLs are non-existent web page addresses that are generated by Large Language Models (LLMs). These URLs may look plausible but lead to 404 errors when clicked. This phenomenon occurs when...

Hallucination refers to instances where generative AI models produce outputs that are factually incorrect or fabricated, such as inventing nonexistent information or citing false references. Technique...

HowTo schema structures step-by-step instructions, making procedural content more retrievable. AI assistants often favor cleanly structured how-to instructions for action-oriented answers.

Hybrid Retrieval blends lexical methods (like BM25) with semantic methods (like vector similarity) to retrieve a more complete and relevant set of documents. This approach mitigates weaknesses of eith...

Intent Velocity is a new metric for the AI search era that measures how quickly a user moves from initial curiosity to conversion. In the context of AI search, users often have a higher intent when th...

Use internal links to cluster content around core entities (topics, products). Consistent anchor text and hub pages improve entity clarity and retrieval strength.

JSON-LD is a structured data format used to annotate pages with machine-readable facts using schema.org vocabularies. Clear schema boosts entity understanding, enables richer knowledge extraction, and...

A knowledge graph is a structured representation of entities and their relationships. For AI search, mapping your domain into entities and links helps models understand context, attributes, and connec...

Large Entity Optimization (LEO) is a new approach to AI search optimization that focuses on how a brand or entity is represented across various AI models, rather than just focusing on keywords. The go...

Large Language Models (LLMs) are advanced artificial intelligence models, such as OpenAI's ChatGPT, Anthropic's Claude, and Google's Gemini. They are trained on massive datasets of text and code, enab...

Learning to Rank is a machine learning approach used to train ranking models for search systems. These models use features like text similarity, authority signals, and query-document relevance to orde...

LLM Answer Coverage measures the number of distinct questions or prompts that a specific piece of content helps a Large Language Model (LLM) to resolve. This metric indicates the breadth of utility an...

Machine-Validated Authority is a modern form of authority recognized by AI systems, serving as an alternative to traditional domain authority and backlink profiles. It refers to the recognition and tr...

Meta-ExternalAgent is a user agent observed for Meta’s external content fetching related to AI features. Keeping critical content accessible can support inclusion in future assistant experiences.

MLOps (Machine Learning Operations) applies DevOps principles to machine learning workflows. It covers the full model lifecycle, from training and validation to deployment, monitoring, and governance,...

A multi-surface strategy ensures your brand appears where engines source answers: articles, docs, videos, forums, and social Q&A. Meeting engines on each surface increases total retrievability and rec...

Multimodal AI refers to systems capable of understanding and generating multiple data modalities, such as text, images, and audio. In search, this allows more flexible querying (including voice or ima...

Multimodal Content refers to content that incorporates multiple formats, such as text, images, audio, and video. As AI models become increasingly multimodal, they will be able to understand and proces...

Normalized Discounted Cumulative Gain (NDCG) is a ranking metric that accounts for the position of relevant items: higher-ranked relevant results are weighted more heavily. It's commonly used to evalu...

A Neural Reranker is an advanced component in an AI search model's ranking pipeline that uses a neural network to re-evaluate and re-order the initial set of retrieved search results. After an initial...

Open Graph helps social platforms display rich previews, while schema.org provides deeper, machine-readable semantics favored by AI retrieval. Both matter, but schema.org is more directly impactful fo...

Passage indexing stores and retrieves sub-document passages rather than whole pages. It increases granularity and the likelihood that specific answers are found and cited.

PerplexityBot is Perplexity.ai’s crawler used to index sources for its answer engine. Ensuring it can access your site increases chances of being cited in Perplexity’s answers.

Personalization tailors answers to user preferences and history, but must respect consent, data minimization, and regional regulations. Brands should design opt-in experiences and avoid over-collectio...

Precision measures the percentage of retrieved documents that are relevant to a query, while Recall measures the percentage of all relevant documents that were retrieved. Together, they provide a bala...

Clear tier names, feature matrices, and currency/region details make pricing pages highly retrievable. Include dateUpdated and FAQs to align with freshness and intent needs.

Product schema annotates product details (name, price, specs, reviews). For AI search, product-rich data increases the chance your offerings appear in comparison answers, buyer guides, and AI recommen...

Programmatic GEO refers to the strategy of using automated processes to create and optimize a large volume of content for Generative Engine Optimization (GEO). A prime example is creating thousands of...

Prompt Engineering is the process of crafting and refining input prompts given to LLMs to guide their responses. Effective prompts can determine the quality, accuracy, style, and structure of AI-gener...

Prompt injection is an attack where content is crafted to override or subvert an AI model's instructions when that content is retrieved and included in context. It can lead to data exfiltration, unsaf...

Q&A pages mirror the question-answer format of AI responses, creating naturally chunkable content. When populated with real customer questions, they rank highly for retrieval and citations.

Query decomposition is the process where an LLM breaks a complex prompt into smaller sub-queries to retrieve targeted evidence. Supporting decomposition with comprehensive, well-scoped chunks improves...

Query Fanout refers to the process where Large Language Models (LLMs) generate multiple related queries based on an initial user prompt to gather more comprehensive information. For example, if a user...

Query rewriting modifies a user’s original question into alternative phrasings to improve recall. Optimizing for paraphrases, synonyms, and related terminology increases the chance your content is mat...

RAG evaluation frameworks like RAGAS measure answer faithfulness, relevance, and context usage by comparing model outputs to retrieved sources. They help teams quantify and improve their retrieval pip...

Reciprocal Rank Fusion (RRF) is an algorithm that combines rankings from multiple retrieval systems (e.g., BM25 and vector search) by summing the reciprocal of each result's rank position. RRF is simp...

Recommendability is the likelihood that an AI will not only cite you but actively recommend your product or solution. It improves with clear product positioning, third-party proofs, and content that m...

Relevance Engineering involves designing content so that AI models can better retrieve, interpret, and cite it. Using semantic scoring, passage optimization, and AI simulation, relevance engineering e...

Relevance Feedback is a technique where user feedback (explicit or implicit) about search results is used to refine subsequent searches. The system uses signals like which results were clicked or mark...

Retrieval-Augmented Generation (RAG) is a technique used by Large Language Models (LLMs) to improve the accuracy and relevance of their responses. Instead of relying solely on their pre-trained knowle...

Retrieval Confidence Score is an internal signal within AI models that reflects the model's estimated likelihood or certainty when selecting a particular content chunk as relevant to a user's query. W...

Robots.txt can allow or disallow specific AI bots by User-Agent (e.g., GPTBot, Google-Extended, CCBot, PerplexityBot, Claude-Web). If you block AI bots, your content may not be retrieved or cited by t...

RRF Rank Contribution refers to the weight or influence a piece of content holds within hybrid ranking systems that utilize Reciprocal Rank Fusion (RRF). RRF is an algorithm that combines results from...

In information retrieval, relevance is a measure of how well retrieved content meets the user's information need. It encompasses factors like topical alignment, timeliness, authority, and novelty. Hig...

Semantic chunking splits content by meaning (e.g., headings, topics) rather than by fixed length. It yields more coherent chunks that LLMs can cite directly in answers.

Semantic Density Score refers to the conceptual richness and depth of meaning within a content block. In AI search, content with high semantic density is packed with relevant entities, concepts, and r...

Semantic HTML involves using HTML tags that convey the meaning and structure of the content, rather than just its presentation. For example, using tags like <article>, <section>, <nav>, and <header> p...

Semantic Search improves relevance by understanding the searcher’s intent and the contextual meaning of terms, rather than relying solely on keyword matching. It helps retrieve results that conceptual...

Server-Side Rendering (SSR) is crucial for AI Search because many Large Language Model (LLM) crawlers cannot effectively render client-side JavaScript. If a website's main content is hidden behind Jav...

XML sitemaps (including video and news variants) help crawlers discover content quickly. For AI bots that prioritize freshness, submitting updated sitemaps and surfacing lastmod timestamps accelerates...

Engines often synthesize from multiple independent sources to reduce bias and improve coverage. Earning mentions across varied domains (news, UGC, docs, research) increases inclusion odds.

Static Site Generation pre-renders pages at build time into static HTML, ensuring full content is available without client-side JavaScript. SSG improves crawlability for AI bots and speeds delivery vi...

Structured Q&A organizes content as direct question-answer pairs with references. It mirrors AI response formats and boosts retrievability.

Tokens are units of text (often subwords) used internally by LLMs. Token budgets affect costs, speed, and how much content can be passed to a model. Optimizing content for compactness and clarity incr...

User-Generated Content (UGC), found on platforms like Reddit, Quora, and YouTube, plays a significant role in AI Search. AI models often value UGC for its authenticity, diverse perspectives, and insig...

Use distinct landing pages and tagging conventions. While many AI answers are zero-click, you can detect AI referrals via user agents, referrers, custom parameters, and downstream behaviors (e.g., hig...

Vector databases are specialized databases designed to store and efficiently query embeddings (numerical representations of data). They are crucial components in AI search systems, particularly for Re...

Vector Index Presence Rate is a Key Performance Indicator (KPI) that represents the percentage of a website's content that has been successfully indexed into vector stores or databases. For content to...

Vector search uses algorithms like k-Nearest Neighbors (k-NN) and Hierarchical Navigable Small World (HNSW) to find semantically similar vectors efficiently. Once candidate vectors are found, similari...

A vectorization pipeline transforms content into embeddings via pre-processing, chunking, and model encoding, then stores them in a vector DB. Clean pipelines reduce noise and improve match quality.

Versioned docs maintain separate pages for major releases (e.g., /v1, /v2) with clear canonical relationships. This structure helps AI models answer version-specific questions accurately without confl...

Yes. Publishing accurate transcripts and captions makes video content indexable and retrievable by text-centric AI systems, increasing inclusion in answers.

VideoObject schema describes videos and their key attributes. Given AI engines' strong reliance on YouTube and video sources, marking up videos and providing transcripts improves multimodal retrieval ...

X-Robots-Tag is an HTTP header that controls indexing and crawling at the resource level, including PDFs and media. It complements robots.txt and meta robots. Clear directives help both classic and AI...

YMYL (Your Money or Your Life) topics affect health, safety, financial stability, or civic information. AI engines demand higher evidence, expert authorship, and stricter grounding for these topics.

YouBot is the crawler associated with You.com’s search and AI products. Visibility in You.com’s answers depends on crawlability and structured content.

Yes. AI answers can shift consideration and intent without a click, leading to direct branded searches, referrals, or assisted conversions. Measure blended impact, not just last-click.

Zero-Click Surface Presence tracks a brand's visibility in smart assistants, AI Overviews, or other answer boxes where users receive direct answers without needing to click through to a website. In th...