Essential terms for understanding Generative Engine Optimization (GEO) and how brands can improve their presence in AI-generated answers.
The practice of improving how a brand appears in AI-generated answers. GEO focuses on visibility, prominence, context, and the sources used to support a brand's presence in tools like ChatGPT, Claude, Gemini, Perplexity & more.
A related discipline focused on optimizing content to appear in direct answer features of AI assistants and search engines, such as featured snippets and conversational AI responses.
While SEO optimizes for ranked lists of links based on keywords and backlinks, GEO optimizes for how information is selected, summarized, and cited by AI models. SEO targets search engine results pages; GEO targets AI-generated answers.
An AI system trained on very large amounts of text that predicts and generates language, and now answers questions directly instead of returning links. ChatGPT, Claude, Gemini, Perplexity and Copilot are all built on one. For a brand, the important property is that an LLM does not look you up in a database: it composes an answer from whatever it retrieves at that moment, which is why the same question asked twice can name different companies. In our own measurement, one question repeated three times in Spain returned 11 distinct cited domains and 4 of them appeared exactly once.
How much an AI system treats a source as reliable on a subject, expressed through whether it retrieves and cites that source. It is not a score any platform publishes, and nobody outside the labs can measure it directly, which is worth saying because plenty of tools imply otherwise. What controlled research supports is narrower and more useful: in the Princeton GEO-bench study across 10,000 queries, adding quotations raised a source's share of the answer by 41%, statistics by 30%, and cited sources by 27%, while keyword stuffing scored below doing nothing.
Whether a specific page carries enough verifiable substance for a model to use it, as opposed to the standing of the site it sits on. The two come apart often: a well-known brand's product page can lose to an independent blog on the same question, because the blog compares and the product page sells. In our measurement of where to book flights, the domains cited in every run in Spain were two metasearch engines, Reddit, Instagram and TikTok, while the large transactional booking sites never reached the stable set.
How often a brand appears across tracked prompts in AI-generated answers. Visibility is the foundational KPI in GEO: if your brand doesn't appear, nothing else matters.
Where a brand appears when mentioned in an AI-generated answer (1st, 2nd, 3rd, etc.). Higher rank means the AI model considers the brand more relevant to the query.
The number of AI-generated answers where a brand appears. Tracking mentions over time reveals whether a brand's presence is growing or declining across AI platforms.
A brand's share of AI mentions compared to competitors. It shows how much of the conversation a brand owns within a specific topic or category in AI-generated answers.
The share of AI systems that name a brand when asked a relevant question. It exists because coverage is not uniform: a company can be recommended by ChatGPT and absent from Claude, and a single-engine report will not show it. The same applies across countries. When we asked which AI visibility tool to use in the United States, Spain and France, twelve runs in total, not one tool was cited consistently in more than one market, and the stable sets did not overlap at all.
How positively or negatively a brand is described in AI-generated answers. Sentiment analysis helps identify whether AI models associate a brand with favorable or unfavorable attributes.
The domains and URLs that AI models cite when generating answers about a brand. Tracking sources helps understand which content influences how AI tools represent a brand.
How often AI models cite or reference specific sources when mentioning a brand. Higher citation frequency indicates that the AI considers those sources authoritative and trustworthy for information about your brand.
A single number summarising how often and how prominently a brand appears across AI answers. Every vendor computes it differently and none publishes the formula, so two tools can report very different scores for the same brand on the same day and both be internally consistent. Treat it as a trend line for your own account rather than a figure to compare across tools or quote externally. The question that decides whether any such score means anything is how many runs sit behind it: at a mention rate near 30%, a hundred runs still gives a confidence interval of 21% to 39%.
When an AI system names or links its source while answering. Attribution matters more than a mention, because a cited source is one the system leaned on rather than merely recalled, and because a link is the only part of an AI answer that can still send a visit. It is also the measurable half: you can check which domains an answer was built from. When we asked which AI visibility tool to use, six runs in the United States, the domains cited every time were Reddit and YouTube, and no funded vendor was cited once.
The natural, consumer-style questions used to query AI models, similar to what people actually ask tools like ChatGPT. For example: "What's the best online store to buy running shoes in Spain?" Prompts can be custom or based on recommended high-intent use cases.
Choosing which questions to measure a brand against, in the words a customer would actually type. It is a different job from the prompt engineering used to get better output from a model: here the prompts are the measuring instrument, so they have to be frozen before a test and left alone. The rule that decides whether a prompt set is worth anything is that the question must not contain your brand name. A query that already names you measures your reputation, not your discovery, and it is the comfortable one to run because it comes back full.
A structured text file placed on a website (similar to robots.txt) that provides LLMs with clear, machine-readable information about a brand, its products, and key facts. EchoWi recommends using llms.txt to improve how AI models understand and represent your brand.
When an AI system states something about a brand that is not true: an invented price, a discontinued product, a merger that never happened. It usually means the model found too little consistent information and filled the gap. The failure is quiet, because a fabricated detail reads exactly like a correct one, and it repeats at scale until the underlying sources change. Checking for it is not the same as checking whether you are mentioned: a brand can be named in every answer and described wrongly in half of them.
Helping an AI system tell your brand apart from everything else that shares its name. This is more common than it sounds and it distorts real decisions. Checking demand for the acronym AEO returns 27,100 monthly searches in the United States, most of which belong to American Eagle Outfitters and to a customs certification. Searching for the AI visibility tool Wellows returns 5,400, of which 4,771 are a compression sock brand called Wellow. If a model cannot separate you from your namesakes, neither can a keyword tool, and both errors cost money.
A structured map of entities and the relationships between them, which search and AI systems use to work out that a name refers to a specific company rather than to a word. It is why a well-connected brand gets described consistently and an ambiguous one gets confused with its namesakes. You cannot edit a knowledge graph directly. What you can do is make the underlying facts consistent everywhere they appear, since the graph is assembled from public sources rather than declared by you.
Machine-readable markup (like Schema.org JSON-LD) added to web pages that helps AI models and search engines understand the content and context of a page. Its effect on AI citations is not established: a controlled study of 1,885 pages against roughly 4,000 controls found no significant increase after schema was added, so treat it as a way to state entities and content explicitly rather than as a citation lever.
How often an AI system refreshes what it knows, either by retraining or by re-retrieving from the live web. It sets the floor on how quickly any change you make can show up, and it varies enormously: a model answering from its training data may be months behind, while one searching the web at request time can reflect a page published this morning. This is the main reason no honest timeline exists for GEO work. The only way to know your own is a frozen question set measured against a control over several weeks.
An AI architecture where the model retrieves relevant documents before generating a response, rather than relying solely on training data. Tools like Perplexity use RAG to provide more current and source-backed answers.
Google's AI-generated answer at the top of a results page, built by running sub-queries against retrieved pages and summarising them, with the sources listed beside it. Coverage is not universal: measuring five categories in Spain in August 2026, two returned no AI Overview at all across thirteen runs, so the first thing to check is whether your category gets one before paying to monitor it.
Tracking a brand across several AI systems at once rather than checking one and generalising. It matters because the engines disagree, and because they are the only place the answer lives: there is no ranking page to inspect. A useful monitoring setup fixes the country and language explicitly on every run, repeats each question several times, and records which sources the answer was built from. Most tools default to the United States and English, so a Spanish question can be answered by the American market with nothing in the report to say so.
How a tool combines results from AI systems that do not answer the same way, and the reason one blended visibility score can mislead. The surfaces differ structurally, not only in score. Measured on 8 August 2026, ChatGPT returned no cited sources at all for definitional questions across nine uncached runs in three unrelated categories, while citing twelve to fifteen domains for the buying question in each of those same categories. Both Google surfaces cited either way. Averaging a surface that cites nobody with one that always cites produces a number no engine would recognise. Normalization worth trusting keeps surface, question type, market and run count separate, and says which of them it held fixed.
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