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
- A separate measure from citation share. In Ahrefs’ 2026 self-promotional experiment, 43% of answers that cited an Evolve conference-promoting page did not mention Evolve, showing why the two observations should not be collapsed into one.
- How it is measured
- Prompts recommended ÷ prompts tracked. Recommendation is kept separate from citation; commercial-data joins are optional and used only when the required integration and data are available.
Prompt set
A defined set of questions used consistently enough to compare answer behavior over time. Its size and weighting should match the business decision being measured rather than a universal 150–400 rule.
- Why it matters
- A repeatable question set provides a practical measurement frame for answer systems that can vary. In Ahrefs’ 2026 experiment, one narrow query condition produced a 66.4% mention rate versus 15.8% for the broader condition; that is experiment-specific evidence for testing query specificity.
- How it is measured
- A question set can be built from first-party research such as support, search, sales and customer-language data, then documented with whatever weighting method the measurement program actually uses.
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
- Inconsistent first- and third-party descriptions can create ambiguity about a business. Reconciliation is a testable hygiene step; no universal cost ranking or three-week effect is asserted.
- 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
- In Ahrefs’ established-brand condition, 94% of new mentions came from third-party pages and 6% from the brand’s own content. That supports measuring external representation without proving a universal weighting formula.
- 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
- In Semrush’s 2025 digital-marketing/SEO study, nearly 90% of ChatGPT Search-cited pages ranked 21+ for related Google queries. In a separate 2024 Semrush AI Overview dataset, cited links overlapped the organic top 10 by roughly 20–26%. These scoped studies show that AI citations can extend beyond page one; they do not define a universal retrieval pool.
- 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
- In Ahrefs’ 2026 experiment, the promoted pages appeared on roughly one-third of eligible days between first and last appearance. That illustrates volatility in that sample; it is not a universal probability for monthly checks.
- 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
- In Semrush’s 10M+ keyword tracking, AI Overview prevalence was 6.49% in January 2025, peaked at 24.61% in July and was 15.69% in November. Separately, Ahrefs’ February 2026 rerun found 58% lower average CTR for the #1 page when an AI Overview was present in its 300,000-keyword informational sample.
- 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 and use vary by system. Google says llms.txt is not used by Google Search for visibility or ranking, and Ahrefs reported that 97% of valid llms.txt files in its May 2026 request-log study received zero requests. Treat it as experimental documentation rather than a proven ranking or citation lever.
- How it is measured
- Not meaningfully measurable in isolation. Treat it as hygiene rather than a lever.