The plain-English AI-visibility glossary.

Audit

A full run of your Requests across your Trading Zones and LLMs, with a report at the end. One audit produces one dashboard of results.

Category

A perception dimension POLIRIS scores your brand on, topics your customers actually care about, such as Customer Experience, Innovation and Technology, Quality and Performance, or Value and Pricing. Categories drive your radar chart, score breakdown bars, competitive position chips, and the tags on every prompt.

Client

A brand you work with in agency mode. Each Client gets its own Workspace, its own Products, and its own reports. Clients can be invited to view their own dashboard in read-only mode.

Dashboard

The main screen with charts and numbers produced by an audit. One audit = one dashboard.

GEO

Generative Engine Optimization. Making sure AI tools mention and understand your brand. The GEO Audit is POLIRIS's core feature: it asks AI models questions on your behalf, listens to the answers, and tells you what it heard across Visibility and Sentiment dimensions.

LLM

"Large Language Model." The AI brains that power modern assistants, ChatGPT, Claude, Gemini, Perplexity, Mistral, Grok, Deepseek, and more. POLIRIS queries multiple LLMs to give you a complete picture of how your brand appears across AI platforms.

Member

A person on your team. Members have one of four roles: Owner (full access including billing), Admin (all settings and members), Editor (create audits, edit products and prompts, publish content), or Viewer (read-only access to dashboards and reports).

Organization

Your top-level POLIRIS account. It holds your branding, members, billing, clients, and all Workspaces. Think of it as the company-level container for everything in your account.

Plan

Your subscription tier: Starter, Growth, or Scale. Each plan determines your monthly credit limits, log retention duration, and access to advanced features like custom domains (Scale) or extended Prospect Client credit pools (Growth).

Poli Agent

The POLIRIS AI assistant. Acts as a helpful teammate who knows every corner of the platform, it can answer questions about your dashboards, run audits on command, summarize long reports into a two-line briefing, suggest and execute next actions, and draft team messages based on the latest audit data.

Product

Something your brand sells, the thing POLIRIS looks for in AI answers. POLIRIS auto-detects Products from your website during onboarding. Multiple Products can be tracked in parallel, each with its own independent GEO Audit scores.

Prospect Client

A brand you don't work with yet, a lead, demo, or sales pitch target. You can run a light audit on a Prospect to show them their GEO score today, then convert them to a real Client with one click when the deal closes. Prospect audits use a reduced credit pool separate from your main monthly budget.

Request

A question you want POLIRIS to ask the AI. Usually written the way a real customer would type it, for example, "What is the best radiology AI solution?" Each Request is linked to one or more Categories and carries a Traffic indicator (High / Medium / Low) showing how often that question is actually asked across LLMs.

Request Prompt

The behind-the-scenes template POLIRIS uses to deliver a Request to an LLM in a structured, consistent way. A Request is what you ask; the Request Prompt is how POLIRIS asks it on your behalf.

Sentiment Audit

Part of the GEO Audit. Analyzes AI responses to determine whether AI talks about your brand positively, neutrally, or negatively, and on which perception dimensions (Categories). Produces an overall tone, a confidence level, a radar chart, and cited source links.

Trading Zone

A country or region where you sell, for example, United States, France, or Japan. AI answers vary from zone to zone, so Trading Zone tells POLIRIS where to look when running your audit. Multiple Trading Zones can be tracked in parallel.

Visibility Area

The combination of Trading Zones, audiences, and LLMs where POLIRIS watches for mentions of your brand. Managed from Settings › Visibility Area, it gives you one place to see every region, audience, and model you cover, and to spot gaps.

Visibility Audit

Part of the GEO Audit. Counts how often AI mentions your brand compared to competitors, measures your competitive rank, shows which sources AI is reading from (Source Intelligence), and reveals a per-LLM score breakdown. Produces an overall visibility score labeled Excellent / Good / Average / Weak.

Workspace

One project inside your Organization, usually one brand or one client. Workspace Settings control which Products to track, which LLMs to query, which Prompts to run, and when to run them. In agency mode, each Client gets its own Workspace.

AI Answer Box / AI Summary / AI Snapshot

AI-generated boxes on results pages that provide a summary or direct answer to a query. They often combine information from multiple sources and include links to reference content.

AI Overview

AI-generated summaries displayed at the top of Google results pages, combining information from multiple sources. They extend the Search Generative Experience and rely on Google's advanced models.

Answer Engine / Response Engine

A system that directly answers questions by computing or generating a response from external data (e.g., WolframAlpha). Unlike traditional search engines, it provides a precise answer rather than a list of links.

Answer Engine Optimization (AEO)

Content optimization to appear in direct answers provided by answer engines or conversational assistants. AEO focuses on content structure, clarity, and reliability so that AI systems can accurately retrieve and cite it.

Chunking

The act of breaking large volumes of text into smaller units ("chunks") to make them easier for language models to process. Chunks may consist of sentences, paragraphs, or passages, preserving relevant context.

Citation

Explicit mention of a source or document in a model's generated response, used to reference the origin of the information. RAG systems and AI Overviews use citations to show which pages were consulted. Structuring your content and providing reliable sources increases the chances of being cited.

Context Window

The maximum number of tokens a model can consider simultaneously. A larger context window allows the model to retain more information and improve response relevance. It determines how much text an LLM can analyze at once and influences chunking and RAG strategies.

Conversational Search

Search where the user asks questions in natural language and receives responses as in a conversation. It replaces keyword-based searches with full queries, relying on language models and context to understand intent and provide evolving answers.

Data Ingestion

The process by which raw data is collected, imported, and integrated into a system for processing or analysis. For language models, this includes gathering texts, cleaning them, and formatting them for training or updating via techniques like RAG.

E-E-A-T

Acronym for Experience, Expertise, Authority, Trustworthiness, Google's content quality evaluation framework. Experience reflects first-hand knowledge; expertise refers to qualifications; authority concerns reputation; trustworthiness relates to site security and transparency. Strengthening these criteria improves content credibility and visibility.

Embeddings

Vector representations (in numerical form) of words, phrases, or objects, organized so that semantically similar items are close in the vector space. Used for semantic search, sentiment analysis, and information retrieval in vector databases.

Enriched Results

Search results that go beyond the traditional blue link, displaying additional data, images, or visuals (reviews, products, events, etc.). Often generated from structured data, they enhance the user experience and can achieve high click-through rates.

Entity / Named Entity

An important element of a text (person, place, organization, event, date, etc.) that can be detected and categorized. Identifying entities feeds knowledge graphs and improves search relevance. In GEO, properly defining and tagging entities enhances recognition by models and increases the likelihood of being cited.

Featured Snippet / Position Zero

A highlighted text snippet at the top of a search results page that provides a concise answer to a query, called "position zero" because it appears before organic results. Selected from well-structured and relevant content, making it important for both SEO and GEO.

Generative AI

A set of models capable of creating text, images, or other original content from existing data. Based on transformer architectures and LLMs, generative AI powers conversational assistants and creative tools.

Generative AI Optimization (GAIO)

The optimization of content to adapt it to generative engines, the conceptual equivalent of GEO. It combines structuring, markup, and semantic relevance so that language models recommend and cite a brand's content. GAIO largely overlaps with GEO.

Generative Engine Advertising (GEA)

Adaptation of advertising strategies to optimize brand visibility and recommendations within AI-generated responses. GEA extends GEO by specifically targeting the commercial recommendations made by generative engines.

Generative Engine Optimization (GEO)

The optimization of digital content to improve its visibility in results generated by AI models, particularly the synthetic answers produced by generative engines. GEO aims to influence the way LLMs retrieve, synthesize, and cite a brand's information in generated responses. Distinct from traditional SEO and AEO.

Generative Search Optimization (GSO)

A set of optimization techniques aimed at ensuring content is effectively recognized by search engines using generative AI. Similar to GEO, AEO, and GAIO, it emphasizes semantic quality and accessibility of information for AI models.

Global Search Optimization (GSO)

Optimization practices aimed at improving content visibility across all search interfaces: traditional search engines, voice assistants, generative engines, etc. Combines SEO, AEO, and GEO to ensure presence in organic results, direct answers, and generated previews.

Google AI Mode

An advanced Google Search experience using deep reasoning and multimodal capabilities to explore a topic in detail. It allows follow-up questions and notably relies on the Query Fan-Out technique to break a query into sub-questions and explore the web in depth.

Grounding / Factual Anchoring

The act of linking an AI model's output to real, verifiable data to ensure factual results. Grounding improves accuracy by basing generation on authentic documents rather than statistical correlations. Often used alongside RAG to combat hallucinations and provide clear citations.

Hallucination (AI)

An inaccuracy or false statement generated by an AI model while appearing plausible. It occurs when the model misinterprets patterns or lacks reliable information. Reducing hallucinations involves techniques such as RAG or grounding, which link output to verifiable sources.

JSON-LD

A serialization format for linked data that allows structured data to be embedded within HTML using JSON. Recommended by Google for implementing Schema.org without modifying visible content. Simplifies markup maintenance and promotes enriched results and knowledge panels.

Knowledge Graph

A knowledge base that represents entities and their relationships as nodes and edges. It stores interconnected descriptions of objects, events, and concepts. Search engines use it to power knowledge panels and generated answers.

Knowledge Panel

A box in search results that presents a summary of information from a knowledge graph (key facts, images, links, etc.) about an entity. Optimizing its presence involves using structured data and precise schemas for relevant entities.

Large Language Model (LLM)

A large language model trained in a self-supervised manner on very large volumes of text. Based on transformer architectures, it contains billions to trillions of parameters and can generate, summarize, translate, and reason over text. LLMs are at the core of conversational agents, code generators, and augmented search systems.

Large Language Model Optimization (LLMO)

Optimization of content so that it is used and cited by generative AI tools (ChatGPT, Perplexity, etc.). It prioritizes authoritative sources, semantically complete content blocks, and formats that are easy to analyze. A variant of AEO and GEO focused specifically on LLMs.

Multimodal Model / Multimodal Search

An AI model capable of understanding and processing multiple types of information (text, image, audio, video) simultaneously. Multimodal search leverages these models to interpret queries combining multiple media and provide integrated results.

Natural Language Processing (NLP)

A field that enables machines to understand, analyze, and generate human language. It covers syntax analysis, translation, entity recognition, sentiment analysis, and more. Recent advances rely on LLMs, embeddings, and pretraining.

Ontology

A formal representation of concepts within a domain and the relationships linking them. It defines classes, properties, and hierarchies to ensure shared understanding for both machines and humans. Ontologies are crucial for knowledge graphs and support semantic search.

Passage

A text segment short enough to be processed or indexed individually. Passages are often created through chunking and serve as units for search or input into generative systems. A well-defined passage improves accuracy by focusing on a specific context.

Prompt

Natural language text that describes the task an AI model should perform. It provides the initial context and can specify the style, format, or information to use. The quality and precision of the prompt directly influence the relevance of the model's responses.

Prompt Engineering

The art of formulating and structuring an instruction (prompt) to obtain more relevant responses from a generative AI model. Optimizing prompts is essential to fully leverage language models in both search and creative applications.

Query Fan-Out

A technique that breaks a question into multiple subtopics and simultaneously sends several queries to find diverse content. It helps capture different intents and retrieve results from the web, knowledge graphs, and other sources. Powers generative search experiences such as Google AI Mode.

Retrieval-Augmented Generation (RAG)

A technique that combines a language model with an information retrieval system to incorporate up-to-date knowledge into generation. The model first consults a set of specific documents before responding, reducing hallucinations and enabling verifiable citations. Allows updating a model without full retraining.

Schema.org

A collaborative initiative led by Google, Microsoft, Yahoo, and Yandex defining schemas for structured data on the internet. Using Schema.org markup in HTML helps generate enriched results, knowledge panels, and accurate AI citations.

Search / Crawl Bots

Programs that automatically browse websites to download and index their content. They allow search engines to discover and update pages, and also collect data for training language models. Publishers can control their activity using robots.txt and certain meta tags.

Search Generative Experience (SGE)

A Google Search Labs experience that uses generative AI to provide quick overviews of a topic with the ability to ask follow-up questions. It paved the way for more conversational search and inspired AI Overviews and AI Mode.

Search Intent

What a user actually wants to achieve when entering a query: informational (seeking a fact), transactional (purchasing or booking), or navigational (accessing a specific site). Understanding intent allows content and responses to be tailored to better meet the underlying need.

Semantic Proximity

A measure of the similarity in meaning between two words, phrases, or entities within a vector space. Calculated from embeddings, it allows finding relevant content beyond simple keyword matching, essential for semantic search and content alignment in answer engines.

Semantic Search

An information retrieval approach that aims to understand intent and contextual meaning rather than relying solely on exact keywords. It leverages embeddings and semantic similarity to deliver more relevant results, particularly for conversational queries.

SEO LLM / LLM SEO

An informal term referring to the optimization of content so that it can be utilized and cited by large language models. Similar to LLMO, it involves structuring content, providing reliable sources, and maximizing models' semantic understanding. Combines traditional SEO and GEO to ensure visibility in generated answers.

Structured Data

A standardized format used to provide information about a page and classify its content (e.g., a recipe: ingredients, cooking time, nutritional value). This markup helps engines understand content and supports the generation of enriched results and use of the page by language models.

Taxonomy

A hierarchical classification used to organize concepts, products, or content into categories and subcategories. In GEO, a consistent taxonomy helps models understand relationships between topics and generate more accurate responses.

Token

A sequence of characters considered a processing unit during tokenization (word, subword, symbol, etc.). Tokenization splits text into tokens, which the model then processes. The number of tokens affects the context window and processing costs.

Transformers

A neural network architecture based on the multi-head attention mechanism. Text is tokenized and embedded, then each token is contextualized via attention weights within a context window. Introduced in "Attention Is All You Need" (2017), transformers form the basis of most large language models today.

Vector Database

A database designed to store and query embeddings (vectors representing the meaning or features of unstructured data). Unlike traditional databases, it handles multidimensional data and searches by similarity. Powers semantic search and RAG by providing relevant documents through vector queries.

YMYL (Your Money or Your Life)

A category of content that can impact users' health, safety, happiness, or financial stability. Google applies higher quality standards to these pages. Publishers must demonstrate strong expertise and trustworthiness to be visible in these sensitive areas.

Zero-Click Search

A search where the answer to the query is provided directly on the results page, without requiring an additional click, via rich snippets, knowledge panels, or generated answers. This trend reduces site traffic in favor of presence in the immediate answer, making GEO and AEO increasingly important.