moreminimore-service-system/skills/geo-optimizer/references/geo-techniques.md

GEO Techniques — Generative Engine Optimization Playbook

Reference for geo-optimizer. Derived from:

• Princeton/GA Tech GEO (KDD 2024, arXiv:2311.09735) — the 9 methods, PAWC metric, GPT-3.5 / Perplexity validation
• AutoGEO (CMU, ICLR 2026) — automated rewriting, GRPO training, utility-preserving rewrite rules
• C-SEO Bench (NeurIPS 2025) — competitive baseline, what survives at scale
• CORE-EEAT / CITE (community frameworks) — operational checklists

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Table of Contents

1. Core Principles
2. The GEO Signal Stack
3. Audit Scoring
4. Rewrite Patterns
5. Evidence Hunt — Finding Real Sources
6. Per-Engine Playbooks
7. AI Crawlability
8. Anti-Patterns
9. Measurement

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Core Principles

1. PAWC drives everything

Position-Adjusted Word Count is the metric the Princeton paper proved correlates with AI citation:

Imp_pwc(c, r) = Σ |sentence| · e^(-pos/total)  /  total_words

The exponential decay is the key: sentence #1 of the AI's answer is worth ~5× sentence #20. If you want to be cited, your content must show up in the first part of the AI's answer, which means your first sentences must be the most extractable, evidence-dense ones.

2. Evidence density > keyword density

Princeton's empirical ranking of techniques by visibility lift:

Rank	Technique	PAWC lift
1	Quotation Addition	+41%
2	Statistics Addition	+30%
3	Cite Sources	+28%
3	Fluency Optimization	+28%
5	Technical Terms	+18%
6	Easy-to-Understand	+14%
7	Authoritative tone	+10%
8	Unique Words	+6%
9	Keyword Stuffing	−8% (hurts)

Best combo: Fluency + Statistics (≥+35%, beats any single technique).

3. Generative engines don't use PageRank

This is the democratization finding from the Princeton GEO paper (arXiv:2311.09735, Table 2): rank-5 sites gained ~+115% visibility with the Cite Sources method while rank-1 sites lost ~30%, averaged across their multi-domain experiment. Numbers are representative of the paper's test setup, not a universal guarantee. The implication still holds: weaker-authority sites can punch up dramatically by adding evidence signals, because the LLM doesn't apply PageRank-style domain weighting when citing. It cares whether your sentence is the most quotable one.

4. Engines diverge

Cross-engine citation overlap is 0.11–0.58 (Princeton + AutoGEO data). Optimize per-engine:

• ChatGPT cites Wikipedia in ~48% of top citations
• Perplexity cites recent web sources, weights freshness
• Gemini leans Reddit/Quora for opinion queries
• Claude weights primary sources and academic citations
• Google AI Overviews mirrors organic top-10 + featured snippets

5. Real evidence wins long-term

Princeton showed fabricated quotes worked against GPT-3.5. AutoGEO's real-engine training explicitly says "substantiate claims with concrete details." Engines have moved on. Build with real sources only.

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The GEO Signal Stack

Four pillars, weighted as in the audit scoring:

Pillar 1 — Evidence Density (35%)

Signal	Target	Why
Numbers with units	≥5 per article	LLMs preferentially extract specific numerics
External citations	≥1 per 500 words, ≥3 source types	Authority + verifiability
Direct expert quotes	≥2 from named individuals	Quotation Addition is the +41% method
Named entities	≥3 with full names + roles	Specificity beats vagueness
First-party data	≥1 original stat or framework	Becomes the only-citable source

Pillar 2 — Structure & Position (25%)

Signal	Target
Direct answer in first 150 words	Required (PAWC)
TL;DR or Key Takeaways near top	≥1 box
Heading hierarchy (H1→H2→H3)	No level skipping, single H1
Comparison/spec data in tables	Required if comparison content
Sequential steps in numbered lists	Required if procedural
FAQ section with question-format H2/H3	Required for informational
Average paragraph length	2–4 sentences
JSON-LD schema	Article minimum, FAQPage if FAQ, HowTo if procedural

Pillar 3 — Authority Signals (25%)

Signal	Target
Author byline	Real name, role, ≥30-word bio
author.sameAs JSON-LD	Wikipedia, LinkedIn, ORCID, Google Scholar
Last updated within 60 days	Recency (3× citation lift per Princeton + amplifying-ai data); 60–90 days is the boundary, target 60
Methodology disclosed	Sample sizes, criteria, dates
Limitations acknowledged	Counter-LLM-hallucination signal
First-party experience markers	"We tested", "Our analysis of N…" — not vague "experts say"
External validators	Featured in / cited by named outlets

Pillar 4 — AI Crawlability (15%)

Signal	Target
robots.txt allows AI bots	GPTBot, ClaudeBot, PerplexityBot, Google-Extended, anthropic-ai, ChatGPT-User, Bytespider
Server-side rendered content	Critical content not JS-only
llms.txt at site root	Optional but adopted by 784+ sites as of mid-2025
HTTPS + HSTS	Required
Canonical URLs	Required
<time> tags + dateModified	Required for freshness signal
Schema validates	Use Rich Results Test

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Audit Scoring

For each item in the signal stack, score:

• Pass (full points) — meets target
• Partial (50%) — partial implementation
• Fail (0) — missing or wrong direction (e.g., keyword stuffing present)

Sum to a 0–100 GEO Score with the pillar weights.

Veto items (auto-cap at 60)

These either kill citation or expose the user to liability:

1. Self-contradictory data — internal inconsistency on the page
2. Title-content intent mismatch — clickbait
3. No identifiable author — anonymous content rarely gets cited as primary source
4. AI crawlers blocked in robots.txt or CDN/WAF
5. Fabricated citations or stats detected — hard fail, not a cap
6. YMYL content without disclaimers — health/finance/legal without appropriate warnings

GEO Score interpretation

• 80–100 — well-positioned for AI citation; iterate on per-engine playbooks
• 60–79 — solid foundation, missing 1–3 high-leverage signals
• 40–59 — structural fixes needed before per-engine work pays off
• 0–39 — rewrite from outline; current content unlikely to be cited

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Rewrite Patterns

Apply in this priority order. Stop when the content is at quality bar; not every page needs every pattern.

Pattern 1 — Front-Load the Answer

Before:

"In today's rapidly evolving digital landscape, businesses are constantly seeking ways to optimize their online presence. This article will explore the various strategies and considerations involved in [topic]."

After (template — replace bracketed values with real, verified data before publishing):

"[Topic]'s ROI averages [REAL_NUMBER]× ([REAL_SOURCE_WITH_URL], [YEAR]) when implemented with [specific approach]. Three steps drive that lift: [step 1], [step 2], [step 3]. Below: how to implement each in 2 weeks."

The first sentence carries: a specific number, a unit, a real-source citation, a year, and a concrete preview. PAWC will weigh this sentence ~5× any conclusion paragraph. Do not ship the template values — every bracketed value must be replaced with a real, verifiable fact before this content goes live. Run the Evidence Hunt section below to source them.

Pattern 2 — Statistics Addition (real)

Before:

"Many companies struggle with onboarding."

After (template):

"[REAL_PERCENT]% of [defined population] report [specific finding] ([REAL_SOURCE_NAME_WITH_URL], [year], n=[real sample size])."

Rule: every claim that can be quantified, must be. Hunt for the real stat before falling back to vague language. If the stat doesn't exist publicly, follow the "What to do when the stat doesn't exist" section below — never keep the claim as a vague unsourced statement.

Pattern 3 — Quotation Addition (real)

Before:

"Experts agree that retention is more cost-effective than acquisition."

After (template):

"'[Verbatim quote from a real, named person],' [wrote/said] [Real Name] in [Real Publication Title] ([Publisher], [Year]), [one-line context establishing why this person is authoritative]."

A real working example for the retention claim above: Frederick Reichheld & Earl Sasser's "Zero Defections: Quality Comes to Services" (Harvard Business Review, Sep–Oct 1990) is the canonical retention-economics citation. Verify the quote and URL before publishing.

Rule: cite a real person at a real org with a real publication. If you can't find one for the claim, the claim probably isn't load-bearing.

Pattern 4 — Citation Addition (real)

Before:

"Search behavior has shifted toward AI assistants."

After (template):

"[Specific stat]% of [defined activity] now [specific behavior] ([Real Research Firm], [Month Year], [URL]) versus [historical stat] in [comparison year], with [observed pattern]."

Rule: ≥1 citation per 500 words, ≥3 source types per article. Source types include: peer-reviewed papers, government data, industry research firms, named publications, primary first-party data. Every citation must include a URL the reader can click — citations without verifiable URLs do not count toward the density target and trigger the fabrication veto.

Pattern 5 — Fluency Optimization

The +28% lift from this method requires no new facts. It's just rewriting for flow. Apply it last, after you've added evidence.

Rules:

• One idea per paragraph
• Sentence variety: alternate short/medium/long
• Active voice by default
• Cut every word that doesn't earn its place
• Read aloud test — if you stumble, rewrite

Pattern 6 — Schema Markup

Minimum for any article-style content:

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "[H1]",
  "author": {
    "@type": "Person",
    "name": "[Real name]",
    "url": "[Author page URL]",
    "sameAs": [
      "https://en.wikipedia.org/wiki/[Author]",
      "https://www.linkedin.com/in/[handle]",
      "https://orcid.org/[id]"
    ]
  },
  "datePublished": "[ISO date]",
  "dateModified": "[ISO date]",
  "publisher": {
    "@type": "Organization",
    "name": "[Org]",
    "logo": {"@type": "ImageObject", "url": "[Logo URL]"}
  },
  "mainEntityOfPage": "[Canonical URL]"
}

Add FAQPage if FAQ section present. Add HowTo if procedural. Validate at search.google.com/test/rich-results before publishing.

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Evidence Hunt — Finding Real Sources

Before any rewrite, build a source list. Tools in priority order:

1. WebSearch — for recent stats and named studies
2. WebFetch on primary source pages — verify the stat exists at the URL
3. Google Scholar (scholar.google.com/scholar?q=...) — academic
4. Government data portals — data.gov, bls.gov, eurostat.ec.europa.eu, data.gov.uk
5. Named research firms — Pew, Forrester, McKinsey, Gartner, Statista (cite the firm + publication date + report name)
6. Primary publications — NYT, FT, WSJ, The Economist, trade press relevant to the topic
7. First-party data from the user — ask: "Do you have any internal data that supports this claim?" Original first-party data is the strongest GEO signal.

Verification rules

• Every stat must trace to a URL you've actually fetched
• Every quote must come from a real publication you can cite by name
• Every named expert must be a real person at a real org
• If you can't verify, reframe the claim or remove it

What to do when the stat doesn't exist

If you genuinely can't find a real source for a claim, in order of preference:

1. Anchor to a related, verifiable stat — "the broader [parent category] grew 12% in 2024 (Source, URL)" with a real source for the parent number. This is acceptable because the citation is real and the relationship is stated honestly.
2. Run an internal analysis — if the user has data, use it. First-party data is the strongest GEO signal anyway.
3. Drop the claim — if it's not load-bearing, cut it.

Do not keep the claim as a vague directional statement ("growing rapidly", "increasingly common", "many companies"). That violates the vague-entity anti-pattern below — vague unsourced statements are still fabrication-adjacent and dilute the page's evidence density.

Never invent. Not "according to a 2024 study", not "experts estimate", not "surveys show". Real source with URL, or no claim.

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Per-Engine Playbooks

Cross-engine citation overlap is 0.11–0.58. Tailor the strategy.

ChatGPT (OpenAI)

Citation pattern: Wikipedia ~48% of top citations; reputable publications; moderate freshness preference.

Optimization moves:

• Build/maintain a Wikipedia presence for the entity (brand, person, product)
• Get listed in Wikidata with structured properties
• Earn coverage in citations Wikipedia accepts (NYT, FT, BBC, Reuters, industry trade press)
• Strong author-as-entity signaling (sameAs to Wikipedia)
• Comprehensive reference articles outrank thin "answer" pages

Perplexity

Citation pattern: Heavy on recent web; cites primary sources directly; fewer "synthesis" citations.

Optimization moves:

• Recency matters most — pages updated within 90 days outperform
• Original first-party data gets cited disproportionately
• Clear thesis sentences in the first paragraph
• Industry blog content with named author + date stamps
• Tracking: Perplexity Sonar API exposes which URLs were cited (gego repo automates this); use it to verify

Gemini (Google)

Citation pattern: Reddit / Quora prominent for opinion / advice queries; Google search index parity.

Optimization moves:

• Reddit presence: maintain authoritative subreddit comments under named account; AMA-style threads
• Quora answers from credentialed account
• Strong on-page Google SEO — Gemini citations correlate with organic top-10
• Google AI Overviews specifically: structured data + featured-snippet format wins

Claude (Anthropic)

Citation pattern: Primary sources, academic citations, well-structured explanatory content.

Optimization moves:

• Long-form, well-cited articles outperform short-form
• Named author with verifiable credentials in author.sameAs
• Citations to peer-reviewed sources where applicable
• Limitations and methodology disclosed (counter-hallucination signaling)
• Avoid marketing language — Claude weights informational tone heavily

Google AI Overviews

Citation pattern: ~85% overlap with organic top 10 + featured snippets.

Optimization moves:

• Win the featured snippet for the query (definition box, list, table)
• Schema markup (Article, FAQPage, HowTo)
• Direct answer in 40–60 words near top of page
• Page must already rank top 10 organically — GEO doesn't bypass SEO here

Cross-engine moves (do these first)

• llms.txt at site root with content map
• Author entities with strong sameAs linkage
• Original data publications quarterly
• Wikipedia / Wikidata presence for the brand
• Reddit + Stack Overflow + relevant community presence

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AI Crawlability

robots.txt — must allow

User-agent: GPTBot
Allow: /

User-agent: ChatGPT-User
Allow: /

User-agent: ClaudeBot
Allow: /

User-agent: anthropic-ai
Allow: /

User-agent: PerplexityBot
Allow: /

User-agent: Perplexity-User
Allow: /

User-agent: Google-Extended
Allow: /

User-agent: Bytespider
Allow: /

User-agent: Applebot-Extended
Allow: /

User-agent: cohere-ai
Allow: /

User-agent: meta-externalagent
Allow: /

If the user is currently blocking these (often inherited from default "block all bots" templates), this is the single highest-leverage fix.

Optional: llms.txt

Adopted by 784+ sites as of mid-2025; not yet a confirmed ranking signal but trending. Place at site root:

# Site Name

> One-paragraph description of the site, what it does, who it's for.

## Core content

- [Page Title](URL): One-line summary
- [Page Title](URL): One-line summary

## About

- [About](URL)
- [Contact](URL)

## Optional

- [Old content](URL): Archive

CDN / WAF

Cloudflare, AWS WAF, and Akamai often block AI bots by default. Verify in the CDN dashboard separately from robots.txt — robots.txt being permissive doesn't help if the WAF returns 403.

Server-side rendering

JS-only content (CSR-heavy SPAs without prerendering) is invisible to most AI crawlers. Use Next.js / Nuxt / Astro / Remix server rendering or static generation for any page that should be cited.

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Anti-Patterns

These either don't work or actively hurt:

Hard fails (will cause penalties or removal)

• Fabricated citations / quotes / stats — see Step 5 of SKILL.md
• Hidden text optimization — old SEO trick; AI engines detect and demote
• Doorway pages — single-purpose pages targeting near-duplicate queries
• AI-generated mass content with no human review — both Google and AI engines now penalize
• PBN backlink networks — CITE framework veto item

Soft fails (waste of effort)

• Keyword stuffing — Princeton: −8% PAWC. Stop.
• Generic AI-language intros — "In today's rapidly evolving landscape…" Cut.
• Vague entities — "a leading company", "experts say", "studies show". Specify or remove.
• Unsupported superlatives — "the best", "the most comprehensive". Either back with data or cut.
• Filler paragraphs — every paragraph must earn its place
• Redundant H2s covering the same subtopic — cannibalizes extraction

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Measurement

GEO without measurement is a vibe. Set up at minimum:

Citation tracking

• gego — open source (Go), self-host. Schedules prompts across OpenAI, Anthropic, Gemini, Perplexity, Ollama; regex-matches brand mentions; Perplexity Sonar URL capture is unique. Repo: https://github.com/AI2HU/gego — clone, follow README to set API keys and run the cron scheduler.
• llmopt — open source (Go + React), self-host. Richer multi-pillar scoring (LLM knowledge testing, AEO content scoring, video authority via YouTube transcripts, Reddit authority, search visibility), MCP integration for Claude Code/Desktop. Repo: https://github.com/jonradoff/llmopt — clone, follow README to configure API keys and start the dashboard.
• Manual baseline — every 2 weeks, run 5 brand queries + 5 category queries against ChatGPT, Claude, Perplexity, Gemini. Log: cited (Y/N), position in answer, sentiment.

Content KPIs (per page)

• GEO Score (this skill's audit)
• Citations per AI engine, per query
• Position in AI answer (1st sentence, 1st paragraph, body, footer)
• Click-through from AI answer (if engine surfaces source links)
• Organic traffic to the page (control variable)

Brand KPIs

• Share of Model — % of category-query AI answers mentioning brand
• Cross-engine coverage — % of monitored engines citing brand
• Sentiment in AI answers — positive / neutral / negative
• Wikipedia presence + Wikidata edit recency

Monthly review

• Which content is being cited? Why? (extract the pattern, replicate)
• Which content was optimized but isn't cited? Why? (audit fail mode)
• Which queries does the brand never appear in? (off-site authority gap?)
• Which engines diverge most from the others? (engine-specific playbook not yet running)

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Quick reference: Do / Don't

Do

• Front-load the answer in first 150 words
• Add real stats with units, sources, dates
• Quote real named experts from named publications
• Cite ≥1 external source per 500 words from ≥3 source types
• Update content every 60 days for competitive queries, 90 days minimum for stable topics
• Allow all major AI crawlers in robots.txt + CDN
• Add Article + FAQPage + HowTo schema as appropriate
• Build Wikipedia / Wikidata / Reddit presence
• Track citations across all four major engines
• Publish original first-party data quarterly

Don't

• Fabricate stats, quotes, citations, or expert names
• Keyword-stuff (−8% PAWC, actively hurts)
• Use vague entities ("experts say", "studies show")
• Block AI crawlers in robots.txt or WAF
• Ship JS-only content without SSR/SSG
• Treat GEO as identical to SEO (different signals, different weights)
• Optimize for one engine and assume the others follow
• Skip the author byline + sameAs linkage