# SEO Geo > Generative Engine Optimization (GEO) — audit and implementation for AI search surfaces: Google AI Overviews and AI Mode, ChatGPT search, Perplexity, Bing Copilot. Covers AI-crawler accessibility, question-based citability scoring, passage extraction, server-side rendering checks for AI bots, llms.txt (with the primary-source caveat on real support), agent-friendly page checks, and a 0-100 scoring rubric. Use when user says "AI Overviews", "AI Mode", "SGE", "GEO", "AI search", "LLM optimization", "Perplexity", "AI citations", "ChatGPT search", "agent-friendly", or "AI visibility". Triggers on: GEO, AEO, AI Overviews, AI Mode, generative engine optimization, llms.txt, AI crawler, citability, Perplexity, ChatGPT search. - Skill: `pixartseu/seo-geo` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add pixartseu/seo-geo` - Raw SKILL.md: https://api.skillmd.com/api/skills/pixartseu/seo-geo/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: PIXARTSeu (https://skillmd.com/u/pixartseu) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/pixartseu/seo-geo --- # AI Search / GEO Optimization GEO is the practice of making content **extractable and citable** by AI search surfaces. This skill is the technical/audit + implementation half: crawler access, render strategy, passage citability, and a falsifiable scoring rubric. For the broader content-strategy and AI-visibility-monitoring side (competitive citation tracking, content briefs, getting cited as a source over time), see the **ai-seo** skill — cross-reference it, don't duplicate it here. ## Primary Source: Google's AI Optimization Guidance Google's own position (Search Central): **optimizing for generative AI search is still SEO.** "AEO" and "GEO" are mostly rebranded labels for the same fundamentals — quality content, crawlable/indexable pages, good structure, unique value. Frame GEO findings as **SEO fundamentals applied to AI-search surfaces**, not as a separate discipline. Things Google has explicitly said do *not* move the needle (treat community advice that contradicts this with skepticism, and note the contradiction in any report): - `llms.txt` is **not** a citation/ranking signal for Google's AI search. - Artificially chunking content for "AI parsing" is unnecessary. - AI-rephrasing existing copy to sound "LLM-friendly" does not help. - Mention-farming / coordinated brand-mention campaigns are not a ranking lever. The durable test for any page is the classic quality framing — **Who** made it (real expertise/authorship), **How** it was made (original effort, not spun), and **Why** it exists (to help users vs. to game search). If a tactic fails the Who/How/Why test, drop it. ## Relationship between the two Google AI surfaces Google runs **two distinct citation engines**, and they cite different URLs: | Surface | Selection behaviour | Optimize for | |---------|--------------------|--------------| | **AI Overviews** | Strongly ranking-correlated — cites pages that already rank well | Classic SEO + passage optimization | | **AI Mode** | Weakly ranking-correlated; broader pool (~9 domains cited/query) | Freshness, entity authority, citable passages beyond position 5 | AI Mode and AI Overviews reach the same *conclusion* most of the time but cite the same *URLs* a small minority of the time (Ahrefs, large query-pair study). Treat them as separate surfaces and score both: ranking well in classic Search feeds AI Overviews, but AI Mode draws from a broader pool where freshness and entity authority outweigh raw position. ## Key Statistics (directional, verify before quoting to a client) | Metric | Value | Source | |--------|-------|--------| | AI Overviews reach | ~1.5B users/month, 200+ countries | Google | | AI Overviews query coverage | 50%+ of queries | Industry data | | AI Mode monthly users | 1B+ | Google | | AI-referred sessions growth | 527% (Jan–May 2025) | SparkToro | | ChatGPT weekly active users | ~900M | OpenAI | **Brand mentions correlate ~3x more strongly with AI visibility than backlinks** (Ahrefs, Dec 2025, 75k brands). Of the mention signals, YouTube mentions show the strongest correlation (~0.737); Domain Rating from backlinks is weak (~0.266). Only ~11% of domains are cited by *both* ChatGPT and Google AIO for the same query — platform-specific optimization is real. --- ## GEO Scoring Rubric (0–100) Score each dimension 0–100, then weight. Every dimension has a **falsifiability check**: how you would know it failed, plus the leading indicator to watch. | # | Dimension | Weight | |---|-----------|--------| | 1 | Citability | 25% | | 2 | Structural readability | 20% | | 3 | Authority & freshness | 20% | | 4 | Technical accessibility (AI crawlers + render) | 20% | | 5 | Multi-modal content | 15% | `final = round(0.25·cite + 0.20·struct + 0.20·auth + 0.20·tech + 0.15·multi)` Bands: **80–100** AI-ready · **60–79** competitive, gaps remain · **40–59** significant work · **<40** largely invisible to AI surfaces. ### 1. Citability (25%) Run passage scoring against **boilerplate-stripped main text** (strip nav, header, footer, sidebars — use your own extractor/crawler), not raw HTML, so chrome doesn't dilute the signal. - Optimal self-contained answer block: **~134–167 words.** - Front-load it: a large share of AI citations come from the first ~30% of a page (SE Ranking) — put the most citable answer near the top, not below the fold. - Direct answer in the **first 40–60 words** of each section. - "X is…" / "X refers to…" definition patterns score high. - Specific facts/statistics with source attribution; unique data points. - Penalize: vague generalities, buried conclusions, opinion without evidence. **Falsifiability:** *Failed if* an AI surface, asked the page's target question, answers without citing the page despite the page ranking on p1. *Leading indicator:* of the top question-headings, what % have a clean, extractable 40–60-word answer block immediately under them? Below ~50% predicts low citation. ### 2. Structural readability (20%) - Clean H1→H2→H3 hierarchy; no skipped levels. - **Question-based headings** that mirror real query phrasing (see citability scoring below). - Short paragraphs (2–4 sentences); lists for steps/multi-item; tables for comparative data; FAQ Q&A blocks. - Penalize: wall-of-text, inconsistent hierarchy, no lists/tables. **Falsifiability:** *Failed if* a heading outline extracted from the DOM reads as topical labels ("Overview", "Features") rather than questions a user types. *Leading indicator:* ratio of question-form headings to total headings. ### 3. Authority & freshness (20%) - Author byline with real credentials; Organization + Person entity signals. - Citations to primary sources (studies, official docs, data). - Entity presence: Wikipedia/Wikidata, Reddit, YouTube, LinkedIn (`sameAs`). - **Freshness is high-leverage.** Recent content (under ~3 months) is markedly more likely to be cited; pages left stale 6+ months lose citation eligibility (SE Ranking, 1.3M-citation study). A scheduled refresh program — with a real `dateModified` change, not a touched timestamp — is one of the best GEO plays. - Penalize: anonymous authorship, missing dates, no sources. **Falsifiability:** *Failed if* the most valuable pages have `dateModified` older than 6 months with no substantive update. *Leading indicator:* median age of top-traffic pages. ### 4. Technical accessibility (20%) **AI crawlers generally do NOT execute JavaScript** — content that only appears after client hydration is invisible to most of them. Server render it. - SSR/RSC vs client-only: is the answer in the initial HTML? - AI crawler access in `robots.txt` (see table below). - "Agent-friendly" basics: stable URLs, real `` links (not JS-only click handlers), text content in HTML rather than canvas/image-only. - `llms.txt` presence (reported, **zero citation weight** — see caveat). **Falsifiability:** *Failed if* `curl`-ing the URL (no JS) returns a shell without the primary answer text. *Leading indicator:* ratio of extractable-text bytes in raw HTML vs. post-hydration DOM. ### 5. Multi-modal content (15%) Pages with relevant images/video/charts/interactive elements show materially higher AI selection rates. Check for text + relevant media, supporting structured data, and that media has real alt/captions (text the crawler reads). **Falsifiability:** *Failed if* a how-to/comparison page is text-only where the query clearly wants a visual. *Leading indicator:* presence of at least one captioned, relevant media element per major section. --- ## Question-based citability scoring (per heading) AI answers are question-shaped. Score each section heading and its lead passage: 1. **Is the heading a question** (or trivially rephrasable as one matching a real query)? Headings like "How do I…", "What is…", "X vs Y" are prime. 2. **Does the first sentence answer it directly** in 40–60 words, self-contained? 3. **Is there a quotable fact/number with attribution** in the block? 4. **Is the block extractable** without surrounding context? Score each 0/1, average over headings → section citability sub-score. This is the fastest lever: rewriting H2/H3s as questions and adding a direct lead answer raises citability without new content. --- ## AI Crawler Access (robots.txt) | Crawler | Owner | Purpose | |---------|-------|---------| | GPTBot | OpenAI | ChatGPT web/search | | OAI-SearchBot | OpenAI | OpenAI search features | | ChatGPT-User | OpenAI | ChatGPT browsing on user request | | ClaudeBot | Anthropic | Claude web features | | PerplexityBot | Perplexity | Perplexity AI search | | Google-Extended | Google | Gemini/Vertex training opt-out token | | CCBot | Common Crawl | Training data (often blocked) | | Bytespider | ByteDance | TikTok/Douyin AI | **Recommendation:** allow GPTBot, OAI-SearchBot, ChatGPT-User, ClaudeBot, PerplexityBot for AI-search visibility. Note that `Google-Extended` is a training opt-out token — blocking it does *not* remove you from AI Overviews/AI Mode (those follow normal Googlebot crawling). Block CCBot / training-only crawlers only if licensing policy requires it. ### Next.js robots config (App Router) ```ts // app/robots.ts import type { MetadataRoute } from 'next' const SITE = process.env.NEXT_PUBLIC_SITE_URL ?? 'https://example.com' const AI_SEARCH_BOTS = ['GPTBot', 'OAI-SearchBot', 'ChatGPT-User', 'ClaudeBot', 'PerplexityBot'] export default function robots(): MetadataRoute.Robots { return { rules: [ { userAgent: '*', allow: '/' }, // Explicitly allow AI search crawlers (overrides any future global tightening) ...AI_SEARCH_BOTS.map((userAgent) => ({ userAgent, allow: '/' })), // Block training-only crawler if licensing requires it: { userAgent: 'CCBot', disallow: '/' }, ], sitemap: `${SITE}/sitemap.xml`, } } ``` --- ## Server-side rendering: the make-or-break check Because AI crawlers don't run JS, the citable answer must be in the **initial HTML payload**. On our stack this means rendering it in a Server Component (default), not gating it behind a `'use client'` boundary that fetches on mount. ```tsx // app/[locale]/guides/[slug]/page.tsx — RSC: answer is in the HTML, no JS needed import { getTranslations } from 'next-intl/server' import { getGuide } from '@/lib/payload' export default async function GuidePage({ params }: { params: Promise<{ slug: string; locale: string }> }) { const { slug } = await params const guide = await getGuide(slug) // server fetch — rendered before send return (

{guide.title}

{/* Lead answer block: 40–60 words, self-contained, first thing in the DOM */}

{guide.summary}

{guide.sections.map((s) => (
{/* Question-form heading mirrors the target query */}

{s.question}

{s.directAnswer}

{s.body}
))}
) } ``` Verify with a JS-disabled fetch (use your own crawler/tooling, or `curl -A "GPTBot" `): the answer text must be present in the raw HTML. --- ## llms.txt — implement, but no false hope The `/llms.txt` convention lets a site advertise its structured content to agents. **Primary-source caveat:** Google has stated it is *not* a citation or ranking signal for AI search, and server-log audits show major AI search systems rarely fetch it. Report its presence in an audit but assign it **zero citation-ranking weight**. It is cheap and harmless to ship (some agent tooling reads it), so treat it as hygiene, not a lever — never promise visibility gains from it. ``` # Example /llms.txt # Acme Docs > Developer documentation for the Acme platform. ## Core - [Quickstart](https://example.com/docs/quickstart): 5-minute setup - [API reference](https://example.com/docs/api): Full endpoint reference ## Key facts - Founded 2019, EU-hosted, GDPR-compliant ``` You can serve it statically (`public/llms.txt`) or generate it from the CMS: ```ts // app/llms.txt/route.ts — generated from Payload content import { getDocsIndex } from '@/lib/payload' const SITE = process.env.NEXT_PUBLIC_SITE_URL ?? 'https://example.com' export const revalidate = 3600 export async function GET() { const pages = await getDocsIndex() const body = [ '# Acme Docs', '> Developer documentation for the Acme platform.', '', '## Core', ...pages.map((p) => `- [${p.title}](${SITE}${p.path}): ${p.summary}`), ].join('\n') return new Response(body, { headers: { 'Content-Type': 'text/plain; charset=utf-8' } }) } ``` --- ## Structured data for AI discoverability (RSC, JSON-LD) Emit entity signals server-side. Keep `dateModified` honest — bump it only on a real content change so the freshness signal stays trustworthy. ```tsx // Inside the RSC page — Article + Person + Organization signals function JsonLd({ guide, site }: { guide: Guide; site: string }) { const data = { '@context': 'https://schema.org', '@type': 'Article', headline: guide.title, datePublished: guide.publishedAt, dateModified: guide.updatedAt, // real modification date only author: { '@type': 'Person', name: guide.author.name, jobTitle: guide.author.role, sameAs: guide.author.profiles, // LinkedIn, Wikipedia, etc. }, publisher: { '@type': 'Organization', name: 'Acme', sameAs: [`${site}`, 'https://www.linkedin.com/company/acme'], }, } return