# AEO — Answer Engine Optimization Reference for `seo-validate` Category 6 (GEO/AEO). Companion to [geo-guidelines.md](geo-guidelines.md). Focuses on optimizing content to appear in AI-generated answers — not just ranked pages. All AEO findings are severity `INFO` — emerging practice, no known penalties. --- ## 1. What is AEO Traditional SEO ranks pages. AEO targets AI-synthesized answers that cite or paraphrase your content without requiring a click. **Key answer engines:** | Engine | Mechanism | |--------|-----------| | Google AI Overviews | Retrieval-augmented summarization over organic index | | Bing Copilot | GPT-4 grounded on Bing index; inline citations | | Perplexity | Real-time retrieval + LLM synthesis; explicit source cards | | ChatGPT Search | OpenAI web retrieval; used in ChatGPT Plus | **Difference from classic SEO:** AI engines do not rank pages against each other — they extract the most useful fragment. A page ranked #8 with a direct-answer paragraph can beat #1 in AI responses. --- ## 2. Content Structure for AI Consumption ### Direct-answer paragraphs (first-sentence-answer pattern) AI engines extract the first 1–3 sentences after a heading as the answer to the implied question. Put the fact first. ```html

What is a canonical URL?

A canonical URL is the preferred page version search engines should index when near-duplicates exist. Specify it with <link rel="canonical" href="..."> in <head>.

What is a canonical URL?

When building websites, you often encounter situations where multiple URLs serve similar content...

``` ### FAQ schema with concise Q&A pairs `FAQPage` is the highest-precision signal. Each `acceptedAnswer` is extracted as a direct response to its `name` question. Pair with matching visible HTML. ```json { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "How do I add a canonical tag in Next.js?", "acceptedAnswer": { "@type": "Answer", "text": "In App Router: export const metadata = { alternates: { canonical: 'https://example.com/page' } }. In Pages Router: use ." } }] } ``` Rules: question text = natural-language query; answer = self-contained under 300 chars for voice engines. ### HowTo schema with numbered steps ```json { "@context": "https://schema.org", "@type": "HowTo", "name": "How to configure robots.txt for a Next.js site", "step": [ { "@type": "HowToStep", "position": 1, "name": "Create the file", "text": "Add robots.txt to public/." }, { "@type": "HowToStep", "position": 2, "name": "Add directives", "text": "Set User-agent, Disallow, Allow, and Sitemap URL." } ] } ``` Match with a visible `
    ` — never schema without HTML steps. ### QAPage schema for single-question articles Use `QAPage` for knowledge-base and support docs structured around one primary question. Include `author` + `answerCount` fields. ### Entity-first writing Define the subject before explaining it. AI engines build knowledge-graph connections from entity definitions. ```html

    Core Web Vitals

    Core Web Vitals are three Google-defined UX metrics: LCP, INP, and CLS. They are used as ranking signals in Google Search since 2021.

    ``` --- ## 3. Technical Signals ### llms.txt / llms-full.txt The `llms.txt` convention (analogous to `robots.txt` for AI agents) lets agents discover curated content. Serve at domain root. ``` # /llms.txt > ExampleCo builds developer tooling for SEO automation. ## Docs - [API Reference](https://example.com/docs/api/llms.txt): Full API docs - [Getting Started](https://example.com/docs/start/llms.txt): Setup guide ``` `llms-full.txt` = concatenated full-text for one-shot context loading. ### robots.txt AI bot directives Known AI crawler `User-agent` strings: | Bot | Owner | Purpose | |-----|-------|---------| | `GPTBot` | OpenAI | ChatGPT training + search | | `OAI-SearchBot` | OpenAI | ChatGPT Search real-time retrieval | | `ClaudeBot` | Anthropic | Claude training | | `anthropic-ai` | Anthropic | Claude real-time retrieval | | `PerplexityBot` | Perplexity | Real-time answer grounding | | `Google-Extended` | Google | Gemini training (NOT AI Overviews) | | `Googlebot` | Google | Organic + AI Overviews | Blocking `Googlebot` blocks AI Overviews. Block `Google-Extended` to opt out of Gemini training only. ``` User-agent: GPTBot Allow: /docs/ Disallow: / User-agent: Google-Extended Disallow: / Sitemap: https://example.com/sitemap.xml ``` ### Sitemap with lastmod Include `` reflecting actual content change dates — not today's build date. AI engines prefer recently-updated sources. ### Clean HTML semantics AI parsers rely on heading hierarchy. Rules: - One `

    ` per page — becomes the answer title in AI responses. - `

    ` maps to extractable sub-questions. - Never skip heading levels. - Use `
    `, `
    `, `
    ` over `
    ` wrappers. --- ## 4. Content Quality Signals ### E-E-A-T | Signal | Implementation | |--------|---------------| | Experience | First-person case studies, original screenshots, tested procedures | | Expertise | Author byline + `Person` schema with `sameAs` links (LinkedIn, GitHub) | | Authoritativeness | `Organization` schema with `sameAs` to Wikidata/Crunchbase | | Trustworthiness | HTTPS, `dateModified` on articles, correction notices | ```json { "@context": "https://schema.org", "@type": "Article", "headline": "How to Optimize robots.txt for AI Crawlers", "author": { "@type": "Person", "name": "Jane Doe", "sameAs": ["https://github.com/janedoe", "https://linkedin.com/in/janedoe"] }, "datePublished": "2026-03-01", "dateModified": "2026-04-10" } ``` ### Citation-worthy content - Statistics with sources: "76% of developers use React (State of JS 2024)" — not "most developers." - Dated claims: include year on every statistic. - Expert quotes in `
    ` with named speaker. - Outbound links to primary sources (w3.org, ietf.org, schema.org, developers.google.com, developer.mozilla.org, arxiv.org) — AI engines treat authoritative outbound links as a credibility signal. --- ## 5. Grep Patterns for Automated Detection All patterns below yield `INFO` + `heuristic` findings. ### Missing FAQ schema on FAQ-like content ``` Detect: (?i)(frequently asked questions|faq|common questions) in *.html,*.tsx,*.jsx,*.vue,*.svelte,*.astro Then check: "FAQPage" in same file or its layout Flag if: FAQ heading found, no FAQPage schema present ``` ### Missing llms.txt ``` Glob: llms.txt in public/, static/, site root Flag if: not found ``` ### robots.txt missing AI bot directives ``` File: robots.txt Pattern: GPTBot|ClaudeBot|PerplexityBot|Google-Extended|OAI-SearchBot|anthropic-ai Flag if: robots.txt exists but no AI-specific User-agent rules found ``` ### How-to headings without HowTo schema ``` Detect: (?i)]*>how (to|do|can|should)\s in *.html,*.tsx,*.jsx,*.vue,*.svelte,*.astro Then check: "HowTo" in same file or layout Flag if: how-to heading found, no HowTo schema present ``` ### Missing author/date metadata on articles ``` Detect: (?i) in HTML ``` --- ## Checklist (Category 6 AEO additions) - [ ] `llms.txt` present at domain root. - [ ] `robots.txt` has explicit AI bot directives. - [ ] Sitemap `` reflects actual content change dates. - [ ] FAQ content uses `FAQPage` schema with self-contained answers. - [ ] How-to content uses `HowTo` schema with `step` array + visible `
      `. - [ ] Single-question pages use `QAPage` schema. - [ ] Articles have `datePublished` + `dateModified` in JSON-LD. - [ ] Author bylines use `Person` schema with `sameAs` links. - [ ] Statistics include inline source citations with year. - [ ] `

      ` headings phrased as natural-language questions where applicable. - [ ] Entity terms defined on first use. --- ## References - llmstxt.org spec: https://llmstxt.org - OpenAI GPTBot docs: https://platform.openai.com/docs/gptbot - Google-Extended opt-out: https://developers.google.com/search/docs/crawling-indexing/overview-google-crawlers - Google E-E-A-T guidance: https://developers.google.com/search/docs/fundamentals/creating-helpful-content - Schema.org FAQPage: https://schema.org/FAQPage - Schema.org HowTo: https://schema.org/HowTo - Schema.org QAPage: https://schema.org/QAPage - Schema.org Article: https://schema.org/Article - Princeton/Georgia Tech GEO paper (Aggarwal et al., 2023): https://arxiv.org/abs/2311.09735