A type of search that uses artificial intelligence to understand what someone is asking and write them a direct answer, usually with links back to sources that influenced the answer.

Any AI system that responds to a question with prose rather than a page of ranked results. ChatGPT, Gemini, Perplexity, Copilot, Claude and Google's AI surfaces are all answer engines, and each one reaches its own conclusions from its own sources. A near-synonym is generative engine.

The discipline of engineering content to become the cited source in AI-generated responses. Where SEO tried to win a position on a page, AEO tries to win a sentence inside an answer. Also called GEO, LLMO or AI SEO. Noble and Profound both use AEO, which is the one piece of vocabulary this glossary is willing to settle.

Google's AI-generated summary above the traditional results, assembled from multiple sources. Google reported more than 2.5 billion monthly users in May 2026, and Pew found people clicked a traditional result in 8% of visits with an AI summary against 15% without. When you continue the conversation with the overview, you move into Google's AI Mode.

The question a person types or speaks to an AI system. In AEO the prompt replaces the keyword as the unit of measurement. It's longer and far more specific about intent. Profound's study of ChatGPT intent across more than 50 million prompts breaks down what people actually ask for.

A search that ends without anyone visiting a website. The AI answer supplies enough that users don't need to click through to the pages the information came from, so those publishers see a fraction of the traffic they used to. For brands, being in the answer is the only way to reach the buyer. Profound's behavioral study with Kevin Indig and Clickstream Solutions found 92.8% of AI shopping tasks ended with no meaningful click to the open web.

The first-party content you control and can edit today. Your product pages, comparison pages, docs, blog and FAQs.

Everything said about you on sites and accounts you don't control. Review sites, Reddit, trade press, listicles and competitor comparison pages, which collectively drive far more of your AI visibility than most teams expect. AI answers lean on this outside corroboration, so being the only site that says you're the best is worth close to nothing. Profound's citation-source analysis breaks the mix down by category and platform.

A model trained on enormous volumes of text that predicts language, and in doing so writes the answers your buyers read. Every answer engine sits on top of one.

Everything the model learned before release. It's fixed until the company trains a new model, so anything you changed after that date exists for the model only if retrieval finds it. The date after which a model's built-in knowledge stops is called the training data cutoff.

The step where the AI reads live web pages while answering instead of relying only on training. You can watch it happen as the "searching the web" moment before the answer appears, and it's the mechanism that lets fresh content influence answers in days rather than years. Also known as RAG (Retrieval-Augmented Generation).

Building the answer out of pages the AI just fetched, instead of out of what it absorbed during training. The citations under an answer are grounding you can see: each one is a claim the model is pinning to a source rather than producing from memory. When nothing external is holding it to a fact, that's where made-up answers (or hallucinations) come from. For brands, this is the whole reason offsite work matters: when an answer is grounded, what the sources say is what the answer says.

The store of pages an AI system has already ingested and searches through when it answers. Each company keeps its own, which is why ChatGPT and Claude can cite completely different sources for the same question.

The bot that fetches pages across the web to build an index or a training set. If a crawler can't reach or render your page, the model never learns the page exists.

When a page gets fetched again and the stored copy is replaced with the current version. Publishing a correction does nothing until a crawler returns, which is why fixing an outdated page is a two-step process you only control half of.

The delay between a source going live or being updated and AI answers reflecting the change. Pickup lag is why AEO reporting needs a few weeks of patience before you conclude a tactic failed.

An answer engine's habit of expanding one user prompt into several background searches before answering. Profound's analysis of 10,000 prompts found roughly 1.4 to 2 fan-out queries per prompt.

The natural spread in AI responses when you run the same prompt repeatedly. It's the single most important reason to measure AI visibility with repeated sampling instead of spot checks, because one manual check tells you almost nothing.

AI systems retrieve pieces of pages, not whole pages. Semantic chunking means splitting at the end of an idea, which typically looks like each heading as a question with a complete answer after it. A section that can stand alone can be cited alone, which makes this the most portable AEO tactic available.

The defined set of questions you track because they're the ones your buyers ask. Choosing this list is the highest-leverage decision in an AEO program, and most teams rush it. Curate for commercial relevance rather than volume: fifteen questions your buyers actually ask beat a hundred they don't.

A prompt that names your company or product, such as "is Profound the best AI analytics software?" Branded prompts test what the models believe about you, not whether they know you exist.

A prompt about a problem or category that names no company, such as "best AI analytics software." This is where shortlists get formed and where visibility is genuinely won or lost.

How often a prompt actually gets asked. Two different numbers share this name, so check which one you're being shown: how many real people asked it, measured from panels of tracked AI users, or how many times a vendor's own tool ran the prompt to sample answers. Only the first tells you anything about demand.

Running a fixed set of prompts across AI platforms on a schedule to measure visibility, citations, sentiment and accuracy. Without a schedule you have anecdotes, and with one you have a baseline you can defend. This is the core of Profound's Answer Engine Insights.

A source an AI tool credits in an answer, shown as a link the reader can click. Citations reveal which content the engines trust in your category, which makes them the most actionable data AI search produces.

Any page or document the system retrieved to build the answer, whether or not it appears as a visible link. Retrieved-but-uncited sources still shape what the answer says about you.

Any instance of your brand being named in an AI answer, with or without a link. Mentions are the raw material of share of voice, and a mention without a link still moves a shortlist.

Accurate, relevant content about your brand inside a source the AI already cites. This is the unit of work in offsite AEO, because improving what a trusted source says about you is faster than becoming a trusted source.

Getting your brand added to an article the AI already cites for your buyers' questions but which currently omits you. It's the highest-leverage offsite play available, since the source has already earned the citation.

Correcting an existing mention that's inaccurate, out of date, or positions you poorly. Usually more urgent than earning a new placement.

A reciprocal arrangement where two brands agree to mention each other in their content. It works best on content that's genuinely relevant to both, and written to the same standard as any other authentic brand mention.

Prompts and topics where AI engines cite your competitors but not you. This is the most directly actionable output of AI search measurement, because it's a to-do list disguised as a report.

Whether and how often your brand appears in AI answers to the questions your buyers actually ask. Visibility is the presence question, and it's the floor of an AEO program rather than the ceiling. It's measured as a percentage of tracked answers, which some vendors call presence rate or brand presence. It's the cleanest and least manipulable metric here, and a good default for executive reporting.

How AI answers characterize your brand: the adjectives, the category they place you in, and the use cases they attach to you. It goes beyond sentiment, because an answer can be positive about you and still describe a company you don't recognize.

Your share of all brand mentions across your tracked prompts, compared against every competitor. If a hundred brand mentions show up across your prompts and twelve are yours, that's 12%. If two new competitors start showing up, your percentage drops even though you haven't lost anything. Read it as a share: your percentage can fall in a month where your actual visibility rose.

How often a specific page or domain is cited as a proportion of all citations. It can be read at either page or domain level, which changes the number substantially. It's the metric to watch when deciding which third-party sites deserve your PR budget. "Share of sources" and "source visibility" are frequently used as synonyms, which is fine as long as you don't mix them in one chart.

Your rank relative to other brands named in the same answer. Being mentioned fourth in a list of ten isn't the same result as being mentioned first, and presence-only metrics erase that distinction.

Whether an answer portrays your brand positively, negatively or neutrally, usually reported as a percentage for each. Because models compress opinion from across the web, AI sentiment is a distilled reading of your reputation rather than a survey of it. Profound tracks it as Sentiment.

A percentage comparing your real pricing, positioning and product details against what AI actually says about you. It's the metric that turns AEO from a marketing exercise into a brand-risk one.

Putting the direct answer at the top and the supporting detail below. Retrieval systems extract passages and models weight early text more heavily, so burying the conclusion is an unforced error. Content that states its key facts early survives a crowded context window.

The unique data, original research or firsthand insight a page adds beyond what's already published. Prioritizing first-party data is one of the pillars of a strong onsite strategy.

How recently a page was published or meaningfully updated. Answer engines favor current sources, and stale pages teach models stale facts about your pricing and product. In controlled tests where a fake publication date was the only thing changed, all seven models tested promoted the "newer" passage, reversing up to 25% of preferences between passages judged equally relevant. Updating existing pages is usually higher return than publishing net-new content, because the citation is already there and only the facts are wrong.

Deep, interlinked coverage of a subject that convinces systems you're a definitive source on it. Breadth without depth reads as a content farm, and depth without breadth reads as a single lucky post.

Whether AI bots can actually reach and parse your pages. That means clear headings, real HTML and plain text, rather than text baked into images or assembled by scripts. Many AI crawlers don't execute JavaScript, so content that only appears after scripts run may be invisible to them regardless of how good it is.

Visits arriving at your site from an AI platform. It's still a small share of total traffic and it's growing fast. Profound's guide to connecting AI referral data to Google Analytics covers how to actually see it.

The structural difference between the influence AI search has and the influence your analytics can prove. It's why AEO reporting leans on visibility and citation metrics rather than sessions.

An increase in people searching your brand name directly. When an answer names you without linking you, branded search is often where the effect actually shows up, though Profound has tracked ChatGPT sharply increasing the share of responses containing a link.

Links from other sites to yours. Models don't read them as votes, so their relevance to AI search is indirect: they drive organic rank, and organic rank predicts citation. The effect is real but weak. Across 75,000 brands, backlink counts correlate near zero with AI visibility and domain rating 0.27 to 0.33, while brand mentions correlate 0.66 to 0.71. Links passing no SEO value perform almost identically to ones that do. Domain authority, a third-party estimate of a domain's overall standing, is useful for triage but isn't a metric any AI system actually uses.

A model repeating facts about you that were true when it was trained and aren't true now: old pricing, retired products, abandoned positioning. Refreshed crawlable content is the only real antidote, and it's slower than anyone wants.

Manipulating third-party listicles and comparison content to skew AI recommendations. Effective, cheap, and the reason competitor comparison pages deserve monitoring. This is the opposite of an authentic brand mention.