The 32 key terms you need to master your brand's visibility in generative AIs. Reviewed and validated by the Pulsari team.
The set of techniques that allow a brand to appear directly in answer engine responses (featured snippets, Google AI Overviews, voice assistants). AEO targets the immediate answer, without a click.
Generative answers displayed above Google search results, produced by Gemini. They synthesize multiple web sources into a cited paragraph. Being mentioned in an AI Overview represents a new Everest of SEO.
The level of thematic specialization perceived by a search engine. The more your site covers a topic in depth and breadth, the more generative AIs consider you a legitimate source to cite.
An explicit or implicit mention of your brand or website in a generative AI response. Citation is the new visibility metric: it replaces the traditional Google ranking.
OpenAI's conversational assistant, based on GPT-4, GPT-4o, GPT-5. The first mainstream LLM and de facto search engine for millions of users who no longer visit Google for certain use cases.
A family of LLMs developed by Anthropic, known for reasoning quality, safety, and long context windows. Used by Pulsari as the reference AI for visibility audits.
The maximum number of tokens an LLM can process in a single pass (prompt + response). A larger window allows reasoning over more content, thus potentially citing more sources.
Google's quality criteria for evaluating content. Historically tied to SEO, they are becoming decisive for AIs seeking reliable sources to cite in their responses.
A numerical vector that captures the semantic meaning of text. AIs use embeddings to retrieve the most relevant documents before generating a response (see RAG).
An SEO approach centered on entities (brands, people, places, concepts) rather than keywords. LLMs reason in entities, not character strings: this is the native language of GEO.
An answer block displayed at position zero on Google, extracted directly from a page. Being a featured snippet is one of the best precursors to being cited by generative AIs.
Retraining an existing LLM on a specific dataset to give it new knowledge or a particular style. Not directly accessible for GEO, but it explains why certain niches are better covered.
The set of techniques that allow a brand to be cited and well-positioned in AI-generated responses (ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews). GEO is to SEO what SEO was to the paper directory: the next revolution.
Google DeepMind's family of LLMs, natively integrated into Google Search (Google AI Overviews), Workspace, and Android. One of the most critical engines to audit for any brand.
A technique that forces an LLM to base its response on real documents or URLs, and to cite its sources. Grounding = transparency, and every grounded source is a citation opportunity for brands.
When an LLM generates inaccurate, fabricated, or outdated information. Hallucinations undermine AI credibility and push publishers to improve grounding, thereby multiplying citations.
A structured data format recommended by Google to enrich a web page with metadata understood by search engines and AIs. A central element of technical GEO.
A relational database of entities and facts used by Google and other engines. Generative AIs rely on similar graphs to validate their responses. Being listed is a strategic asset.
A large-scale language model trained on billions of parameters. GPT, Claude, Gemini, Llama, Mistral are all LLMs. They are the engines that generate the responses in which your brand must appear.
A text file placed at the root of a website to give LLMs a clear, citable overview of its content, similar to robots.txt for traditional crawlers. An emerging GEO standard.
An LLM-based conversational search engine that always cites its sources. The perfect playground for measuring AI visibility since citations are visible and trackable.
A question or instruction sent to an LLM. Generative AIs respond to prompts, not keywords: GEO is about making sure your brand answers the right prompts in your niche.
An architecture that combines document retrieval and text generation. Perplexity and Google AI Overviews are RAGs: they fetch pages in real time, then summarize them.
A standardized structured data vocabulary co-created by Google, Bing, Yahoo, and Yandex. Organization, Article, FAQPage, HowTo: the most useful types for GEO.
An approach that optimizes for meaning and context, not exact keywords. Essential preparation for GEO: LLMs understand meaning, not repetition.
A search engine results page. Historically dominated by 10 blue links, it now includes AI Overviews, featured snippets, People Also Ask: all formats that GEO must target.
A metric that measures how frequently a brand is cited by AIs in its niche, compared to its competitors. Pulsari is built around this metric.
Information tagged with a recognized vocabulary (Schema.org) that gives both search engines and AIs an explicit understanding of a page's content. Without structured data, you are flying blind in GEO.
A text fragment (roughly 3 to 4 characters in English) that an LLM processes. API pricing is calculated per token, and the context window is measured in tokens. Understanding tokens helps size a GEO strategy.
Elements that reinforce a site's perceived credibility: identified authors, sources, dates, certifications, press mentions, complete Organization schema. Key to becoming a preferred AI source.
A database optimized for storing and querying embeddings. The cornerstone of RAG systems. AIs use these databases to instantly retrieve the most relevant documents for a given question.
A synthetic measure between 0 and 100 that evaluates a brand's visibility in generative AIs for its niche. The Pulsari score combines the number of mentions, citation quality, and positioning versus competitors.