{ "format_version": 1, "canonical": "https://rotgar.com/resources/what-is-answer-engine-optimization", "path": "/resources/what-is-answer-engine-optimization", "title": "What Is Answer Engine Optimization (AEO)? — Rotgar", "h1": "What Is Answer Engine Optimization?", "description": "Answer engine optimization (AEO) structures content so AI Overviews and assistants extract and attribute a direct answer. What it covers, and what it isn't.", "language": "en", "kind": "informational", "source_html_sha256": "d2f9c5ebd5e0edf116af4f91b153bb2dc414cb89ab847091c46bc79433dd7fa1", "content_markdown_sha256": "bec315868b0e723f0ccb19e911ab0c02d379a88fc9a0bec86dfbd76fcc454342", "alternatives": { "markdown": "https://rotgar.com/machine/pages/resources/what-is-answer-engine-optimization.md", "json": "https://rotgar.com/machine/pages/resources/what-is-answer-engine-optimization.json" }, "content": { "markdown": "Resources\n\n# What Is Answer Engine Optimization?\n\n![Three-stage diagram of answer extraction: user question, 40–60-word extracted passage, attributed source, each with its verification test and a side note on snippet controls that block extraction](https://rotgar.com/images/package-v2/geo-ai/what-is-answer-engine-optimization-hero.png)\n\nузел 1 «User question\n\n**TL;DR**\n\n- AEO makes one passage on your page the answer an engine can lift and attribute. The unit of optimization is a self-contained chunk of text, not a ranking position.\n- Google states there are [“no additional requirements to appear in AI Overviews or AI Mode, nor other special optimizations necessary”](https://developers.google.com/search/docs/appearance/ai-features) — a page must be indexed and eligible to be shown with a snippet, and “no special schema.org structured data that you need to add.”\n- Answer surfaces absorb clicks: [Pew Research](https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/) tracked 68,879 Google searches by 900 US adults in March 2025 — 18% produced an AI summary, click-through was 8% with one versus 15% without, and 1% clicked inside the summary.\n- AEO is not a markup trick: Google’s answer to whether you can mark up a page for featured snippets is [“You can’t”](https://developers.google.com/search/docs/appearance/featured-snippets), and since September 2023 [FAQ rich results](https://developers.google.com/search/docs/appearance/structured-data/faqpage) appear only for “well-known, authoritative government and health websites.”\n- In our own pilot (Perplexity, August 3, 2026), the definitional prompt returned a definition plus a comparison table, citing Semrush, Wikipedia, Coursera and Mailchimp: extractable formats win the answer, established entities win the attribution.\n\nAnswer engine optimization (AEO) is the practice of structuring content so answer engines — AI Overviews, voice assistants, chat-based search — can extract a direct, self-contained answer and attribute it to your brand. The term is older than the current AI wave: it grew up around featured snippets and voice assistants, and the market now applies it to the same work for AI-generated answers (SERP observation, July 2026). For a medical practice the stakes are concrete — a patient asks an assistant a question, and the assistant names one provider.\n\n## How do answer engines work?\n\nAn answer engine takes a question, finds a passage that answers it, checks that the passage survives being torn out of its page, and attributes it to a source it can identify. Every AEO decision serves one of those three steps: be retrievable, be self-contained, be identifiable. Nothing in the sequence rewards volume.\n\n1. **Extracts a passage.** The engine locates the section that matches the question — not the page as a whole. Page-level relevance gets you considered; passage-level clarity gets you used.\n2. **Checks self-sufficiency.** The passage has to make sense outside the page. If your answer depends on the paragraph above it or on a heading that supplies the missing noun, it fails the test.\n3. **Attributes a source.** The engine credits an entity it understands. Ambiguity about who published the answer — inconsistent names, missing authorship, credentials nowhere in machine-readable form — is a reason to cite somebody else.\n\nA **citation** is that attribution: the answer naming or linking your brand. An **entity** is who you are in machine-readable form — your organization’s consistent identity across your site, profiles, and third-party mentions.\n\nUnderneath all three sits a plain eligibility requirement. Google’s AI features documentation states that “a page must be indexed and eligible to be shown in Google Search with a snippet.” No AEO tactic outranks that sentence: a page blocked from snippets is not competing for answers at all.\n\n## What changed between featured snippets and AI Overviews?\n\nThe mechanism is inherited; the economics are not. Featured snippets extracted one passage and attributed it to one page. AI Overviews synthesize an answer from several sources and cite several links — so the goal shifts from *being the answer* to *being one of the sources inside it*, on a surface where far fewer people click through.\n\n| Dimension | Featured snippet (classic) | AI Overview / AI Mode (current) | What it changes for AEO |\n| --- | --- | --- | --- |\n| Source of the answer | One passage lifted from one ranking page | A synthesis assembled from multiple pages | Your passage competes as one input among several, not as the single winner |\n| Attribution | One title and link above the result | Several cited links beside and inside the generated text | Being cited is more achievable; being the only source is far less likely |\n| Eligibility | Indexed page, eligible to show with a snippet | Same, stated explicitly: indexed and snippet-eligible, no additional requirements | There is no separate AEO technical layer to buy |\n| Markup that triggers it | None — “You can’t” mark up a page for featured snippets | None — “no special schema.org structured data that you need to add” | Schema supports understanding; it does not trigger inclusion |\n| Opt-out controls | `nosnippet`, `data-nosnippet`, `max-snippet` | The same controls, plus `noindex`, apply to AI features | Snippet controls remain the only levers with a documented effect |\n| Click behavior | Answer shown, some clicks retained | 18% of searches carried an AI summary; 8% click-through versus 15% without; 1% clicked inside it (Pew, March 2025) | Presence in the answer becomes an end in itself |\n| Reporting | Search Console Performance, “Web” search type | Same — AI feature traffic sits inside overall Search traffic under “Web” | AI Overview impressions cannot be isolated in Search Console today |\n\nTwo consequences follow. A page that never gets extracted loses twice: no visit, and no mention in the answer that replaced the visit. And because eligibility is ordinary Search eligibility, the fastest wins are repairs to pages that already rank — restructuring answers, not building new assets. The step-by-step version is our [how to show up in AI Overviews](https://rotgar.com/resources/how-to-show-up-in-ai-overviews) page.\n\n## What makes an answer extractable?\n\nExtractability is a property of a block, not of a page. A block passes when a stranger could paste it into a chat window and read it as a finished answer: question in the heading, answer in the first 40–60 words, format matching the content type, publisher identifiable. The checklist below is the version we run in audits.\n\n| Layer | What to check | Done when |\n| --- | --- | --- |\n| Crawl and index | The page is indexed and eligible to be shown with a snippet | URL returns 200, appears in Search Console, no `noindex` and no `nosnippet` on the answer block |\n| Question | The H2 is phrased as the question a real person asks | The heading matches a query from Search Console, autocomplete, or intake calls |\n| Direct answer | The first 40–60 words under the heading answer the question completely | The opening paragraph makes sense with everything else on the page deleted |\n| Chunk boundaries | One block covers one sub-intent and depends on nothing above it | Pull-out test: remove the surrounding sections, the block still reads as a complete answer |\n| Format | Comparisons are tables, procedures are numbered lists, definitions are single sentences | Every comparison stated in prose also exists as table rows |\n| Entity | The organization and author behind the answer are consistent and verifiable | Same name, location, credentials and author across site, profiles and structured data |\n| Structured data | `Article` and `FAQPage` markup describes content the user can actually see | Markup validates and matches visible content — with no expectation of a rich result |\n| Attribution and freshness | Every claim about platform behavior carries a date and a primary source | Numbers link to the publisher of the number; `dateModified` reflects a real edit |\n| Snippet controls | Nothing truncates the answer block | No `max-snippet` shorter than the intended answer; no `data-nosnippet` wrapping it |\n\nThe structured data row is the one most often oversold. Markup is worth doing correctly — but Google restricts FAQ rich results to authoritative government and health sites, and says no special schema is needed for AI Overviews. Schema is clarity for machines, never a switch that turns on citations. The full layer-by-layer version is our [GEO checklist](https://rotgar.com/resources/geo-checklist).\n\n## How is AEO different from GEO?\n\nThey aim at different surfaces and share almost all of the work. AEO targets engines that extract and display one attributed answer; GEO targets generative chat systems that synthesize a recommendation and cite brands inside it. The compact split below covers most decisions.\n\n| | AEO | GEO |\n| --- | --- | --- |\n| **Systems targeted** | Answer engines: AI Overviews, voice assistants, chat-based search | Generative chat systems: ChatGPT, Gemini, Perplexity |\n| **What gets extracted** | A direct, attributed answer passage | A synthesized recommendation |\n| **What success looks like** | Your passage quoted as the answer | Your brand named as the recommendation |\n\nFor the second column: Generative engine optimization (GEO) is the practice of making a brand’s website, content, and entity signals easy for AI systems — ChatGPT, Gemini, Google AI Overviews — to understand, trust, and cite in generated answers. The term page is [generative engine optimization (GEO)](https://rotgar.com/resources/what-is-generative-engine-optimization). In practice the two are one discipline with two aims; the full side-by-side — surfaces, metrics, where the labels came from — is in the [AEO vs GEO — full comparison](https://rotgar.com/resources/aeo-vs-geo).\n\n## What does our own measurement show about answer extraction?\n\nOur pilot supports one uncomfortable AEO conclusion: on definitional questions the answer format is winnable, the citation slot mostly is not. On August 3, 2026 we ran the first live slice of our fixed 16-prompt battery — four prompts on Perplexity, clean anonymous session, English, European IP. Four prompts, one surface, non-US IP: read for structure, not for rates.\n\n- **The definitional prompt** (“What is generative engine optimization?”) returned a definition followed by a comparison table, citing Semrush, Wikipedia, Coursera, Mailchimp and Seer Interactive. The engine reproduced exactly the two formats this page recommends — a one-sentence definition and a table.\n- **The citation slot went entirely to established entities.** No newcomer appeared. For a site with our authority profile, the honest role of a definition page is topical authority and internal routing, not a citation in someone’s chat window.\n- **Category and offer prompts behaved differently.** “Who provides generative engine optimization services?” cited listicles and directories (Clutch’s GEO category, DesignRush) rather than any vendor’s page; “Where can I get an AI visibility audit?” cited Reddit three times. Those are recommendation contexts, won off-site.\n- **Citation rate for our own brand: 0/4** — the expected zero point for a site that had just published this content. The value of a baseline is the delta at re-measurement, not the first number.\n\nThe AEO reading: extraction and attribution are separate wins. Structure decides whether your passage *can* be used; entity strength decides whether it *is* credited. Pages that fix only the first half stay invisible exactly the way the pilot shows.\n\n## What AEO is NOT\n\n- **Not a per-platform trick.** There is no separate “optimize for ChatGPT” hack that survives a model update. The durable work sits at the level of content structure and entity clarity.\n- **Not a replacement for SEO.** Answer engines assemble answers from indexed, snippet-eligible pages — the exact asset classic SEO builds, and Google’s stated guidance for AI features is standard SEO best practice.\n- **Not a schema switch.** Structured data helps machines understand a page; it does not trigger inclusion in AI Overviews, and FAQ rich results have been limited to authoritative government and health sites since September 2023.\n- **Not a guarantee of citation.** No one can guarantee a citation — the decision belongs to the engine. Anyone selling guaranteed AI citations is selling what they do not control.\n- **Not a one-time setup.** Surfaces, formats and citation behavior change quarterly: structure, measure, re-measure, adjust.\n\n## What does AEO mean for a clinic?\n\nFor a clinic, the buying decision starts as a question, and the assistant answers with a name. Patients ask symptom-and-choice questions (“do I need a root canal,” “best dermatologist for acne scars”). Whoever published the extractable answer is in position to be that name.\n\nHealthcare is YMYL territory, where trust signals weigh harder: identified authorship, verifiable claims, real credentials. We write about visibility, never medical or legal advice — and in an AI answer, an unverifiable claim is a reason to route around you.\n\nThe service layer is [AI search optimization for medical practices](https://rotgar.com/medical/services/chatgpt-gemini); when the priority surface is Google specifically, [Google AI Overviews optimization for clinics](https://rotgar.com/medical/services/google-ai-overviews).\n\n## Key takeaways\n\n1. AEO structures content so an answer engine can extract a direct, self-contained passage and attribute it to your brand — the unit is a block of text, not a page or a keyword.\n2. Eligibility is ordinary Search eligibility: indexed, snippet-eligible, no special schema, no additional requirements — stated in Google’s own AI features documentation.\n3. AI Overviews inherit the featured-snippet mechanism but change the economics: 18% of searches carried an AI summary and click-through fell to 8% from 15% (Pew, March 2025), so presence in the answer is an end in itself.\n4. Extractability is testable: question-shaped heading, 40–60-word answer, a block that survives the pull-out test, tables for comparisons, consistent entity, dated sources.\n5. Structured data is clarity, not a trigger — FAQ rich results have been restricted to authoritative government and health sites since September 2023.\n6. Extraction and attribution are separate wins: our August 2026 Perplexity pilot showed definitional answers delivered in exactly the recommended formats, with citations going only to established entities.\n7. AEO and GEO are one discipline with two aims — direct-answer surfaces versus generative recommendations — and no version of either can promise a citation.\n\n## FAQ\n\n### Is answer engine optimization the same as SEO?\n\nNo. AEO builds on the SEO foundation and adds passage-level structure for answer extraction. SEO makes pages rank; AEO makes passages quotable, with attribution. The foundation is shared: Google states that appearing in AI features requires nothing beyond being indexed and snippet-eligible.\n\n### Is AEO the same as GEO?\n\nNot exactly — they aim at different surfaces: answer engines that extract one attributed passage (AEO) versus generative chat systems that synthesize recommendations (GEO). In practice most of the work overlaps, because both rest on the same structure and entity signals. The detailed breakdown is in our [AEO vs GEO](https://rotgar.com/resources/aeo-vs-geo) comparison.\n\n### Does AEO work for ChatGPT and Gemini?\n\nYes — both extract and attribute answers from web sources, but they draw on very different source volumes. [Semrush’s 2026 AI Visibility Index](https://www.semrush.com/news/463141-semrush-releases-expanded-2026-ai-visibility-index-analyzing-126-million-ai-search-prompts/), built on 126 million US AI search prompts from January to April 2026, found ChatGPT cites an average of 15 sources per response while Gemini cites three. The passage-structure work transfers to both; the odds of appearing do not.\n\n### Does schema markup get my page into AI answers?\n\nNo. Markup helps machines understand what a page is and who published it, and is worth doing correctly — but Google’s documentation states no special structured data is required for AI Overviews or AI Mode, and FAQ rich results are limited to well-known, authoritative government and health websites. Schema is clarity, not a lever.\n\n### Can a small medical practice do AEO without an agency?\n\nThe fundamentals are doable in-house: question-shaped headings, direct 40–60-word answers, FAQ blocks built from real client questions, consistent business data across profiles. Teams usually bring in help for measurement across surfaces and for the third-party citation layer that recommendation prompts actually cite.\n\n### How do I know whether my business is cited in AI answers?\n\nTest the prompts your buyers actually use, on a fixed schedule, and record who gets named. The original published protocol specifies a fixed set of 16 buyer-intent prompts tested monthly across Google AI Overviews, ChatGPT, Gemini, and Perplexity, scored by citation rate.\n\nData visual\n\nQuestion to attributed answer\n\nThree-stage answer extraction flow: user question, 40–60-word extracted passage and attributed source, each with its verification test.\n\n1. **User question**Does an indexed, snippet-eligible page cover this question?\n2. **Extracted passage**40–60 wordsDoes the block remain a complete answer alone?\n3. **Attributed source**Is the publisher identifiable and consistent across site, profiles and schema?\n\nBlocked by: noindex · nosnippet · short max-snippet.\n\nStructure decides whether the passage can be used; entity strength decides whether it gets credited.\n\nData visual\n\nClick behavior with and without an AI summary\n\nGoogle click-through comparison: 15 percent of visits without an AI summary, 8 percent with one, and 1 percent clicking a link inside the summary.\n\nSource: Pew Research Center · 68,879 searches · 900 U.S. adults · March 2025\n\nWhen the answer appears on the page, the click stops being the outcome — the mention is.\n\nRelated reading\n", "headings": [ { "level": 1, "text": "What Is Answer Engine Optimization?" }, { "level": 2, "text": "How do answer engines work?", "id": "how-do-answer-engines-work" }, { "level": 2, "text": "What changed between featured snippets and AI Overviews?", "id": "what-changed-between-featured-snippets-and-ai-overviews" }, { "level": 2, "text": "What makes an answer extractable?", "id": "what-makes-an-answer-extractable" }, { "level": 2, "text": "How is AEO different from GEO?", "id": "how-is-aeo-different-from-geo" }, { "level": 2, "text": "What does our own measurement show about answer extraction?", "id": "what-does-our-own-measurement-show-about-answer-extraction" }, { "level": 2, "text": "What AEO is NOT", "id": "what-aeo-is-not" }, { "level": 2, "text": "What does AEO mean for a clinic?", "id": "what-does-aeo-mean-for-a-clinic-or-a-law-firm" }, { "level": 2, "text": "Key takeaways", "id": "key-takeaways" }, { "level": 2, "text": "FAQ", "id": "faq" }, { "level": 3, "text": "Is answer engine optimization the same as SEO?", "id": "is-answer-engine-optimization-the-same-as-seo" }, { "level": 3, "text": "Is AEO the same as GEO?", "id": "is-aeo-the-same-as-geo" }, { "level": 3, "text": "Does AEO work for ChatGPT and Gemini?", "id": "does-aeo-work-for-chatgpt-and-gemini" }, { "level": 3, "text": "Does schema markup get my page into AI answers?", "id": "does-schema-markup-get-my-page-into-ai-answers" }, { "level": 3, "text": "Can a small medical practice do AEO without an agency?", "id": "can-a-small-practice-or-law-firm-do-aeo-without-an-agency" }, { "level": 3, "text": "How do I know whether my business is cited in AI answers?", "id": "how-do-i-know-whether-my-business-is-cited-in-ai-answers" } ], "tables": [ { "caption": null, "rows": [ { "cells": [ { "text": "Dimension", "markdown": "Dimension", "header": true, "colSpan": 1, "rowSpan": 1 }, { "text": "Featured snippet (classic)", "markdown": "Featured snippet (classic)", "header": true, "colSpan": 1, "rowSpan": 1 }, { "text": "AI Overview / AI Mode (current)", "markdown": "AI Overview / AI Mode (current)", "header": true, "colSpan": 1, "rowSpan": 1 }, { "text": "What it changes for AEO", "markdown": "What it changes for AEO", "header": true, "colSpan": 1, "rowSpan": 1 } ] }, { "cells": [ { "text": "Source of the answer", "markdown": "Source of the answer", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "One passage lifted from one ranking page", "markdown": "One passage lifted from one ranking page", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "A synthesis assembled from multiple pages", "markdown": "A synthesis assembled from multiple pages", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Your passage competes as one input among several, not as the single winner", "markdown": "Your passage competes as one input among several, not as the single winner", "header": false, "colSpan": 1, "rowSpan": 1 } ] }, { "cells": [ { "text": "Attribution", "markdown": "Attribution", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "One title and link above the result", "markdown": "One title and link above the result", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Several cited links beside and inside the generated text", "markdown": "Several cited links beside and inside the generated text", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Being cited is more achievable; being the only source is far less likely", "markdown": "Being cited is more achievable; being the only source is far less likely", "header": false, "colSpan": 1, "rowSpan": 1 } ] }, { "cells": [ { "text": "Eligibility", "markdown": "Eligibility", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Indexed page, eligible to show with a snippet", "markdown": "Indexed page, eligible to show with a snippet", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Same, stated explicitly: indexed and snippet-eligible, no additional requirements", "markdown": "Same, stated explicitly: indexed and snippet-eligible, no additional requirements", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "There is no separate AEO technical layer to buy", "markdown": "There is no separate AEO technical layer to buy", "header": false, "colSpan": 1, "rowSpan": 1 } ] }, { "cells": [ { "text": "Markup that triggers it", "markdown": "Markup that triggers it", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "None — “You can’t” mark up a page for featured snippets", "markdown": "None — “You can’t” mark up a page for featured snippets", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "None — “no special schema.org structured data that you need to add”", "markdown": "None — “no special schema.org structured data that you need to add”", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Schema supports understanding; it does not trigger inclusion", "markdown": "Schema supports understanding; it does not trigger inclusion", "header": false, "colSpan": 1, "rowSpan": 1 } ] }, { "cells": [ { "text": "Opt-out controls", "markdown": "Opt-out controls", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "nosnippet, data-nosnippet, max-snippet", "markdown": "`nosnippet`, `data-nosnippet`, `max-snippet`", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "The same controls, plus noindex, apply to AI features", "markdown": "The same controls, plus `noindex`, apply to AI features", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Snippet controls remain the only levers with a documented effect", "markdown": "Snippet controls remain the only levers with a documented effect", "header": false, "colSpan": 1, "rowSpan": 1 } ] }, { "cells": [ { "text": "Click behavior", "markdown": "Click behavior", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Answer shown, some clicks retained", "markdown": "Answer shown, some clicks retained", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "18% of searches carried an AI summary; 8% click-through versus 15% without; 1% clicked inside it (Pew, March 2025)", "markdown": "18% of searches carried an AI summary; 8% click-through versus 15% without; 1% clicked inside it (Pew, March 2025)", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Presence in the answer becomes an end in itself", "markdown": "Presence in the answer becomes an end in itself", "header": false, "colSpan": 1, "rowSpan": 1 } ] }, { "cells": [ { "text": "Reporting", "markdown": "Reporting", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Search Console Performance, “Web” search type", "markdown": "Search Console Performance, “Web” search type", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Same — AI feature traffic sits inside overall Search traffic under “Web”", "markdown": "Same — AI feature traffic sits inside overall Search traffic under “Web”", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "AI Overview impressions cannot be isolated in Search Console today", "markdown": "AI Overview impressions cannot be isolated in Search Console today", "header": false, "colSpan": 1, "rowSpan": 1 } ] } ] }, { "caption": null, "rows": [ { "cells": [ { "text": "Layer", "markdown": "Layer", "header": true, "colSpan": 1, "rowSpan": 1 }, { "text": "What to check", "markdown": "What to check", "header": true, "colSpan": 1, "rowSpan": 1 }, { "text": "Done when", "markdown": "Done when", "header": true, "colSpan": 1, "rowSpan": 1 } ] }, { "cells": [ { "text": "Crawl and index", "markdown": "Crawl and index", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "The page is indexed and eligible to be shown with a snippet", "markdown": "The page is indexed and eligible to be shown with a snippet", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "URL returns 200, appears in Search Console, no noindex and no nosnippet on the answer block", "markdown": "URL returns 200, appears in Search Console, no `noindex` and no `nosnippet` on the answer block", "header": false, "colSpan": 1, "rowSpan": 1 } ] }, { "cells": [ { "text": "Question", "markdown": "Question", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "The H2 is phrased as the question a real person asks", "markdown": "The H2 is phrased as the question a real person asks", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "The heading matches a query from Search Console, autocomplete, or intake calls", "markdown": "The heading matches a query from Search Console, autocomplete, or intake calls", "header": false, "colSpan": 1, "rowSpan": 1 } ] }, { "cells": [ { "text": "Direct answer", "markdown": "Direct answer", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "The first 40–60 words under the heading answer the question completely", "markdown": "The first 40–60 words under the heading answer the question completely", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "The opening paragraph makes sense with everything else on the page deleted", "markdown": "The opening paragraph makes sense with everything else on the page deleted", "header": false, "colSpan": 1, "rowSpan": 1 } ] }, { "cells": [ { "text": "Chunk boundaries", "markdown": "Chunk boundaries", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "One block covers one sub-intent and depends on nothing above it", "markdown": "One block covers one sub-intent and depends on nothing above it", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Pull-out test: remove the surrounding sections, the block still reads as a complete answer", "markdown": "Pull-out test: remove the surrounding sections, the block still reads as a complete answer", "header": false, "colSpan": 1, "rowSpan": 1 } ] }, { "cells": [ { "text": "Format", "markdown": "Format", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Comparisons are tables, procedures are numbered lists, definitions are single sentences", "markdown": "Comparisons are tables, procedures are numbered lists, definitions are single sentences", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Every comparison stated in prose also exists as table rows", "markdown": "Every comparison stated in prose also exists as table rows", "header": false, "colSpan": 1, "rowSpan": 1 } ] }, { "cells": [ { "text": "Entity", "markdown": "Entity", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "The organization and author behind the answer are consistent and verifiable", "markdown": "The organization and author behind the answer are consistent and verifiable", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Same name, location, credentials and author across site, profiles and structured data", "markdown": "Same name, location, credentials and author across site, profiles and structured data", "header": false, "colSpan": 1, "rowSpan": 1 } ] }, { "cells": [ { "text": "Structured data", "markdown": "Structured data", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Article and FAQPage markup describes content the user can actually see", "markdown": "`Article` and `FAQPage` markup describes content the user can actually see", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Markup validates and matches visible content — with no expectation of a rich result", "markdown": "Markup validates and matches visible content — with no expectation of a rich result", "header": false, "colSpan": 1, "rowSpan": 1 } ] }, { "cells": [ { "text": "Attribution and freshness", "markdown": "Attribution and freshness", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Every claim about platform behavior carries a date and a primary source", "markdown": "Every claim about platform behavior carries a date and a primary source", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Numbers link to the publisher of the number; dateModified reflects a real edit", "markdown": "Numbers link to the publisher of the number; `dateModified` reflects a real edit", "header": false, "colSpan": 1, "rowSpan": 1 } ] }, { "cells": [ { "text": "Snippet controls", "markdown": "Snippet controls", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Nothing truncates the answer block", "markdown": "Nothing truncates the answer block", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "No max-snippet shorter than the intended answer; no data-nosnippet wrapping it", "markdown": "No `max-snippet` shorter than the intended answer; no `data-nosnippet` wrapping it", "header": false, "colSpan": 1, "rowSpan": 1 } ] } ] }, { "caption": null, "rows": [ { "cells": [ { "text": "", "markdown": "", "header": true, "colSpan": 1, "rowSpan": 1 }, { "text": "AEO", "markdown": "AEO", "header": true, "colSpan": 1, "rowSpan": 1 }, { "text": "GEO", "markdown": "GEO", "header": true, "colSpan": 1, "rowSpan": 1 } ] }, { "cells": [ { "text": "Systems targeted", "markdown": "**Systems targeted**", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Answer engines: AI Overviews, voice assistants, chat-based search", "markdown": "Answer engines: AI Overviews, voice assistants, chat-based search", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Generative chat systems: ChatGPT, Gemini, Perplexity", "markdown": "Generative chat systems: ChatGPT, Gemini, Perplexity", "header": false, "colSpan": 1, "rowSpan": 1 } ] }, { "cells": [ { "text": "What gets extracted", "markdown": "**What gets extracted**", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "A direct, attributed answer passage", "markdown": "A direct, attributed answer passage", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "A synthesized recommendation", "markdown": "A synthesized recommendation", "header": false, "colSpan": 1, "rowSpan": 1 } ] }, { "cells": [ { "text": "What success looks like", "markdown": "**What success looks like**", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Your passage quoted as the answer", "markdown": "Your passage quoted as the answer", "header": false, "colSpan": 1, "rowSpan": 1 }, { "text": "Your brand named as the recommendation", "markdown": "Your brand named as the recommendation", "header": false, "colSpan": 1, "rowSpan": 1 } ] } ] } ], "links": [ { "text": "“no additional requirements to appear in AI Overviews or AI Mode, nor other special optimizations necessary”", "url": "https://developers.google.com/search/docs/appearance/ai-features", "kind": "external", "download": false }, { "text": "Pew Research", "url": "https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/", "kind": "external", "download": false }, { "text": "“You can’t”", "url": "https://developers.google.com/search/docs/appearance/featured-snippets", "kind": "external", "download": false }, { "text": "FAQ rich results", "url": "https://developers.google.com/search/docs/appearance/structured-data/faqpage", "kind": "external", "download": false }, { "text": "how to show up in AI Overviews", "url": "https://rotgar.com/resources/how-to-show-up-in-ai-overviews", "kind": "internal", "download": false }, { "text": "GEO checklist", "url": "https://rotgar.com/resources/geo-checklist", "kind": "internal", "download": false }, { "text": "generative engine optimization (GEO)", "url": "https://rotgar.com/resources/what-is-generative-engine-optimization", "kind": "internal", "download": false }, { "text": "AEO vs GEO — full comparison", "url": "https://rotgar.com/resources/aeo-vs-geo", "kind": "internal", "download": false }, { "text": "AI search optimization for medical practices", "url": "https://rotgar.com/medical/services/chatgpt-gemini", "kind": "internal", "download": false }, { "text": "Google AI Overviews optimization for clinics", "url": "https://rotgar.com/medical/services/google-ai-overviews", "kind": "internal", "download": false }, { "text": "AEO vs GEO", "url": "https://rotgar.com/resources/aeo-vs-geo", "kind": "internal", "download": false }, { "text": "Semrush’s 2026 AI Visibility Index", "url": "https://www.semrush.com/news/463141-semrush-releases-expanded-2026-ai-visibility-index-analyzing-126-million-ai-search-prompts/", "kind": "external", "download": false } ] }, "structured_data": [ { "@context": "https://schema.org", "@graph": [ { "@type": "Organization", "@id": "https://rotgar.com/#organization", "name": "Rotgar", "url": "https://rotgar.com/", "foundingDate": "2011", "email": "evgeniy.yudin@rotgar.com", "telephone": "+34 605 303 439", "logo": { "@type": "ImageObject", "@id": "https://rotgar.com/#logo", "url": "https://rotgar.com/logo-square.png", "contentUrl": "https://rotgar.com/logo-square.png", "width": 112, 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SEO makes pages rank; AEO makes passages quotable, with attribution. The foundation is shared: Google states that appearing in AI features requires nothing beyond being indexed and snippet-eligible." } }, { "@type": "Question", "name": "Is AEO the same as GEO?", "acceptedAnswer": { "@type": "Answer", "text": "Not exactly — they aim at different surfaces: answer engines that extract one attributed passage (AEO) versus generative chat systems that synthesize recommendations (GEO). In practice most of the work overlaps, because both rest on the same structure and entity signals. The detailed breakdown is in our AEO vs GEO comparison." } }, { "@type": "Question", "name": "Does AEO work for ChatGPT and Gemini?", "acceptedAnswer": { "@type": "Answer", "text": "Yes — both extract and attribute answers from web sources, but they draw on very different source volumes. Semrush's 2026 AI Visibility Index, built on 126 million US AI search prompts from January to April 2026, found ChatGPT cites an average of 15 sources per response while Gemini cites three. The passage-structure work transfers to both; the odds of appearing do not." } }, { "@type": "Question", "name": "Does schema markup get my page into AI answers?", "acceptedAnswer": { "@type": "Answer", "text": "No. Markup helps machines understand what a page is and who published it, and is worth doing correctly — but Google's documentation states no special structured data is required for AI Overviews or AI Mode, and FAQ rich results are limited to well-known, authoritative government and health websites. Schema is clarity, not a lever." } }, { "@type": "Question", "name": "Can a small medical practice do AEO without an agency?", "acceptedAnswer": { "@type": "Answer", "text": "The fundamentals are doable in-house: question-shaped headings, direct 40–60-word answers, FAQ blocks built from real client questions, consistent business data across profiles. Teams usually bring in help for measurement across surfaces and for the third-party citation layer that recommendation prompts actually cite." } }, { "@type": "Question", "name": "How do I know whether my business is cited in AI answers?", "acceptedAnswer": { "@type": "Answer", "text": "Test the prompts your buyers actually use, on a fixed schedule, and record who gets named. The original published protocol specifies a fixed set of 16 buyer-intent prompts tested monthly across Google AI Overviews, ChatGPT, Gemini, and Perplexity, scored by citation rate." } } ] }, { "@type": "DefinedTerm", "@id": "https://rotgar.com/resources/what-is-answer-engine-optimization#defined-term", "name": "answer engine optimization", "description": "AEO makes one passage on your page the answer an engine can lift and attribute. The unit of optimization is a self-contained chunk of text, not a ranking position. Google states there are \"no additional requirements to appear in AI Overviews or AI Mode, nor other special optimizations necessary\" — a page must be indexed and eligible to be shown with a snippet, and \"no special schema.org structured data that you need to add.\" Answer surfaces absorb clicks: Pew Research tracked 68,879 Google searches by 900 US adults in March 2025 — 18% produced an AI summary, click-through was 8% with one versus 15% without, and 1% clicked inside the summary. AEO is not a markup trick: Google's answer to whether you can mark up a page for featured snippets is \"You can't\", and since September 2023 FAQ rich results appear only for \"well-known, authoritative government and health websites.\" In our own pilot (Perplexity, August 3, 2026), the definitional prompt returned a definition plus a comparison table, citing Semrush, Wikipedia, Coursera and Mailchimp: extractable formats win the answer, established entities win the attribution.", "url": "https://rotgar.com/resources/what-is-answer-engine-optimization" }, { "@type": "WebSite", "@id": "https://rotgar.com/#website", "name": "Rotgar — AI Visibility for Clinics", "url": "https://rotgar.com/", "inLanguage": "en", "publisher": { "@id": "https://rotgar.com/#organization" } }, { "@type": "WebPage", "@id": "https://rotgar.com/resources/what-is-answer-engine-optimization#webpage", "name": "What Is Answer Engine Optimization (AEO)? — Rotgar", "description": "Answer engine optimization (AEO) structures content so AI Overviews and assistants extract and attribute a direct answer. 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