Generative engine optimization (GEO)
The practice of getting a brand named inside AI-generated answers — the paragraph ChatGPT, Perplexity, Google AI Overviews or Copilot returns instead of a list of links. Distinct from SEO in that the target is a sentence in a paragraph rather than a position in a list.
- Why it matters
- It is the discipline everything else on this page belongs to. Also called answer engine optimization or AI search optimization; the three names describe the same job.
- How it is measured
- By citation share and recommendation rate across a defined prompt set.
Answer engine optimization (AEO)
A synonym for GEO, more common in B2B marketing teams. Where the two are distinguished at all, AEO usually implies the narrower job of formatting content so a machine can lift a direct answer from it.
- Why it matters
- Mostly a vocabulary difference. If a vendor claims a methodological distinction between GEO and AEO, ask them to state it in one sentence.
- How it is measured
- Identically to GEO.
Citation share
The percentage of prompts in a defined set where a brand is named or linked in the generated answer.
- Why it matters
- The primary scoreboard metric, and the one most tools report. On its own it is misleading — see recommendation rate.
- How it is measured
- Prompts cited ÷ prompts tracked, per engine, weighted by the revenue behind each prompt.
Recommendation rate
The share of prompts where a brand is named as the answer rather than used as a footnote.
- Why it matters
- The more valuable of the two numbers and the one that correlates with pipeline. Ahrefs found 43% of answers citing a page never mentioned that page's brand — a gap invisible to citation share alone.
- How it is measured
- Prompts recommended ÷ prompts tracked. Tracked separately from citation, and joined to the CRM.
Prompt set
A fixed list of 150–400 real buyer questions, each weighted by the revenue of the customers who ask it.
- Why it matters
- The GEO equivalent of a keyword list, and the only stable way to measure something that answers differently every time. Narrow questions outperform broad ones badly: 66.4% mention rate against 15.8% in the Ahrefs data.
- How it is measured
- Built from support tickets, reviews and won/lost CRM notes, clustered by a model, then weighted by revenue.
Entity
The machine-readable identity of a business: its name, category, location, offering and relationships, as expressed consistently across your site, your schema and every third-party profile.
- Why it matters
- Models hedge or miscategorise you while those sources disagree. Reconciling them is consistently the cheapest movement available and usually shows within three weeks.
- How it is measured
- By counting how many distinct category descriptions are in circulation across your own properties and third-party profiles.
Corroboration
Independent sources describing a brand the same way.
- Why it matters
- Assistants weight consensus above self-description. 94% of an established brand's new AI mentions came from third-party pages in the Ahrefs study, against 6% from its own content.
- How it is measured
- By the count of third-party pages that state your category claim, and where those pages rank.
Retrieval pool
The set of pages an assistant actually searches and quotes for a given question.
- Why it matters
- The pool and page one are largely different sets: roughly 90% of ChatGPT citations come from pages ranking 21 or lower, and AI Overview links overlap the organic top ten only 20–26% of the time.
- How it is measured
- By recording the ranking position of every page cited across the prompt set.
Citation volatility
How inconsistently a cited page reappears for the same prompt.
- Why it matters
- Measured at roughly one day in three in the Ahrefs experiment. It is why a monthly manual check has about a one-in-three chance of describing your real position.
- How it is measured
- Days appearing ÷ eligible days, between a page's first and last appearance for a prompt.
AI Overview
Google's generated answer above the organic results.
- Why it matters
- It appeared on 6.49% of searches in January 2025 and 24.61% by July, and cuts clickthrough for the top-ranking page by about 34.5% when present. Your ranking can hold still while the traffic behind it drains.
- How it is measured
- By AI Overview prevalence across your tracked keyword set, and clickthrough on affected terms.
llms.txt
A plain-text file at the root of a site listing its most useful pages for language models, in the spirit of robots.txt.
- Why it matters
- Support is inconsistent and it is not a ranking factor. Genuinely low priority — worth an hour, after the items above, not before.
- How it is measured
- Not meaningfully measurable in isolation. Treat it as hygiene rather than a lever.