# geo.wiki — full content index

> Every published entry page of the Wiki, AI Engines, Playbooks, Research and
> Products collections, in both locales, with its TL;DR. Hub pages, Learn steps,
> Pulse archives and policy pages are not listed here — /llms.txt is the curated map.
> Generated: 2026-09-12 (auto-regenerated at build time).

## Module entry points

- https://geo.wiki/ — Homepage
  - https://geo.wiki/zh (中文)
- https://geo.wiki/learn — Learn (guided paths)
  - https://geo.wiki/zh/learn (中文)
- https://geo.wiki/wiki — Wiki (encyclopedia hub)
  - https://geo.wiki/zh/wiki (中文)
- https://geo.wiki/platforms — AI Engines (per-engine deep dives)
  - https://geo.wiki/zh/platforms (中文)
- https://geo.wiki/playbooks — Playbooks (repeatable GEO methods)
  - https://geo.wiki/zh/playbooks (中文)
- https://geo.wiki/research — Research (annotated papers)
  - https://geo.wiki/zh/research (中文)
- https://geo.wiki/pulse — Pulse (AI-search news digest)
  - https://geo.wiki/zh/pulse (中文)
- https://geo.wiki/products — Products (researched GEO software profiles)
  - https://geo.wiki/zh/products (中文)
- https://geo.wiki/projects — Projects (verified GEO repositories)
  - https://geo.wiki/zh/projects (中文)
- https://geo.wiki/data/projects.json — Projects catalogue (JSON, machine-readable)

## Wiki entries

- https://geo.wiki/aeo-vs-geo — AEO vs GEO — In practice, AEO and GEO describe the same work. AEO emerged in the era of Featured Snippets and voice assistants; GEO became the prevailing term as generative, multi-source answers became the default.
  - https://geo.wiki/zh/aeo-vs-geo (中文)
- https://geo.wiki/ai-content-detection — AI Content Detection — AI engines down-weight patterns associated with low-effort or scaled production, whether AI tools were involved or not. Classifier scores do not control citation or visibility and should not be treated as GEO signals.
  - https://geo.wiki/zh/ai-content-detection (中文)
- https://geo.wiki/ai-crawlers — AI Crawlers — AI crawlers fall into three categories with different access consequences: training, retrieval, and user-triggered agents. Access decisions should be made by category, because blocking retrieval crawlers to prevent training can remove a site from AI answers.
  - https://geo.wiki/zh/ai-crawlers (中文)
- https://geo.wiki/ai-search-timeline — AI Search Timeline (2022–present) — AI search began with ChatGPT on 2022-11-30 and moved through four phases: Shock (Bing Chat and Bard), Fragmentation (SGE, Claude, Perplexity, and the coinage of GEO), Mainstream (AI Overviews and ChatGPT Search), and Stabilization (AI Mode reaching 1B MAU).
  - https://geo.wiki/zh/ai-search-timeline (中文)
- https://geo.wiki/answer-loop — Answer Loop — Every generative answer follows the same four-step runtime loop: query understanding, retrieval, grounding, and answer synthesis. GEO improves the conditions it can influence at each step and helps diagnose failures specific to that step.
  - https://geo.wiki/zh/answer-loop (中文)
- https://geo.wiki/brand-mentions — Brand Mentions — An unlinked brand mention strengthens the model's prior about an entity. Repeated references across the web can influence future answers without providing a link, so mentions and links serve different purposes.
  - https://geo.wiki/zh/brand-mentions (中文)
- https://geo.wiki/chatgpt-user — ChatGPT-User — ChatGPT-User fetches a page when it is needed during a live ChatGPT interaction. OpenAI says robots.txt may not apply and confirms that the token does not determine ChatGPT Search inclusion. Blocking it can prevent the page from appearing in a reader's answer.
  - https://geo.wiki/zh/chatgpt-user (中文)
- https://geo.wiki/citability — Citability — Under GEO Wiki's working definition, citability describes whether a retrieved passage has enough local context and clarity for an AI system to interpret and use it accurately. It is separate from source trust and does not guarantee selection, a citation, or a link.
  - https://geo.wiki/zh/citability (中文)
- https://geo.wiki/citation-vs-mention — Citation vs Mention vs Link — AI answers can credit a source through a citation, a mention, or a link. These outcomes are not interchangeable: using a source and crediting it are separate events, and each form is measured differently, influenced by different factors, and valuable for different reasons.
  - https://geo.wiki/zh/citation-vs-mention (中文)
- https://geo.wiki/content-freshness — Content Freshness — Content freshness combines a page's age (recency) with whether its claims remain true (currency). AI engines favor recent content, but the average AI-cited page is about 2.9 years old. Freshness depends on the query; it is not a reason to publish every week.
  - https://geo.wiki/zh/content-freshness (中文)
- https://geo.wiki/core-web-vitals — Core Web Vitals (LCP/INP/CLS) — Core Web Vitals (LCP, INP, and CLS) are Google ranking signals, not AI-engine signals. Their GEO effect is direct for Google AI Overviews, partial for Bing Copilot, and negligible for ChatGPT Search, Perplexity, and Claude. AI crawler performance is a separate issue.
  - https://geo.wiki/zh/core-web-vitals (中文)
- https://geo.wiki/e-e-a-t — E-E-A-T — E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trustworthiness. Google uses it as a framework for quality raters, not an algorithmic score. For GEO, it addresses whether the source behind a retrieved, liftable passage is trustworthy enough to use.
  - https://geo.wiki/zh/e-e-a-t (中文)
- https://geo.wiki/entity-recognition — Entity Recognition — Entity recognition is how an AI engine maps a brand, product, or author name to the correct known entity. Accurate resolution lets mentions, citations, and markup reinforce the intended entity; ambiguity can send that credit elsewhere or cause it to be discarded.
  - https://geo.wiki/zh/entity-recognition (中文)
- https://geo.wiki/generative-engine — Generative Engine — A generative engine retrieves sources, uses them to ground an LLM, and synthesizes a written answer that may credit those sources. Unlike a search engine, it composes an answer instead of returning documents. This is the system that GEO aims to influence.
  - https://geo.wiki/zh/generative-engine (中文)
- https://geo.wiki/generative-engine-optimization — Generative Engine Optimization — Generative Engine Optimization (GEO) improves the chances that AI systems such as ChatGPT, Perplexity, Google AI Overviews, and Gemini will retrieve your content, use it to support an answer, and cite or mention it. GEO extends SEO; it does not replace it.
  - https://geo.wiki/zh/generative-engine-optimization (中文)
- https://geo.wiki/geo-metrics — GEO Metrics — These 10 core GEO KPIs cover definitions, formulas, SEO equivalents, and the different ways Profound, Otterly, Ahrefs, BrightEdge, and Similarweb define them. The set is a GEO Wiki synthesis, not an industry standard.
  - https://geo.wiki/zh/geo-metrics (中文)
- https://geo.wiki/geo-roi — GEO ROI Models — Generative search separates value from the click, so traditional ROI models miss much of GEO's impact. A three-currency framework measures citation value, substituted traffic value, and brand authority value across B2B SaaS, B2C e-commerce, and media.
  - https://geo.wiki/zh/geo-roi (中文)
- https://geo.wiki/geo-spam-and-manipulation — GEO Spam & Manipulation — Adversarial GEO now appears in both published research and commercial practice. Every published attack succeeds in a single-attacker study, but competition consistently shrinks or reverses the gains. Two of the four documented attack surfaces lie outside your website.
  - https://geo.wiki/zh/geo-spam-and-manipulation (中文)
- https://geo.wiki/google-extended — Google-Extended — Google-Extended is a robots.txt control token, not a crawler. It governs specified Gemini training and grounding uses without affecting Search inclusion or ranking. Search AI appearance and grounding are governed by a separate Search Console control that is still rolling out.
  - https://geo.wiki/zh/google-extended (中文)
- https://geo.wiki/gptbot — GPTBot — GPTBot is OpenAI's training crawler. It collects content that may be used to train future models, but it does not support ChatGPT Search or live answers. Blocking it does not reduce citations, but doing so may mean forgoing a future benefit that cannot be measured or attributed.
  - https://geo.wiki/zh/gptbot (中文)
- https://geo.wiki/json-ld — JSON-LD — JSON-LD is one of three ways to express Schema.org markup. Google recommends it because it stays separate from visible HTML and is easy to maintain. Index-integrated AI parses the markup as structured data, while live-fetch chatbots read it as page text.
  - https://geo.wiki/zh/json-ld (中文)
- https://geo.wiki/knowledge-graph-presence — Knowledge Graph Presence — Knowledge graph presence means having an entity node in a graph an AI engine trusts, such as Wikipedia, Wikidata, or the Google Knowledge Graph. It strengthens the model's prior and aids resolution, but it does not cause citations or become credible through self-declaration.
  - https://geo.wiki/zh/knowledge-graph-presence (中文)
- https://geo.wiki/llmo-vs-geo — LLMO vs GEO — LLMO and GEO are mostly the same thing. They share a goal and nearly identical definitions in practice. LLMO emphasizes the model rather than the answer; only training-corpus inclusion makes it distinct, and GEO excludes that largely uncontrollable work.
  - https://geo.wiki/zh/llmo-vs-geo (中文)
- https://geo.wiki/llms-txt — llms.txt — llms.txt is a convention proposed by Answer.AI in 2024: a curated markdown file at /llms.txt that identifies the pages an LLM should read first. Sites are publishing it, but no major AI vendor confirms using it. It is a low-cost precaution, not a citation channel.
  - https://geo.wiki/zh/llms-txt (中文)
- https://geo.wiki/multilingual-geo — Multilingual GEO — Multilingual GEO examines how AI retrieval, entity matching, passage extraction, and trust signals change across languages. The answer loop stays the same, but source pools, entity binding, chunk structure, and trust pools vary. Source-pool differences matter most.
  - https://geo.wiki/zh/multilingual-geo (中文)
- https://geo.wiki/multimodal-signals — Multimodal Signals — Multimodal signals help AI engines interpret non-text assets such as images, video, audio, and charts. In 2026, text associated with an asset, including alt text, captions, transcripts, and schema, still matters more than pixel-level visual analysis in both retrieval modes.
  - https://geo.wiki/zh/multimodal-signals (中文)
- https://geo.wiki/robots-txt — robots.txt — RFC 9309 defines robots.txt as a voluntary request, not access control. Compliant AI crawlers follow it as documented; noncompliant and spoofed agents may not. Set rules separately for training, retrieval, and user-triggered agents, then verify access at the network layer.
  - https://geo.wiki/zh/robots-txt (中文)
- https://geo.wiki/schema-org-for-ai — Schema.org for AI — Schema.org markup is not a ranking or citation signal. It helps AI systems distinguish entities and parse pages. Index-integrated AI uses it, while live-fetch chatbots read JSON-LD as plain text. Markup can make an entity resolvable, but not a passage citable.
  - https://geo.wiki/zh/schema-org-for-ai (中文)
- https://geo.wiki/seo-vs-geo — SEO vs GEO — SEO and GEO share the same foundation: crawlability, real expertise, clear structure, and authoritative mentions. SEO aims for a ranked link that earns a click; GEO aims for a citation or mention in a synthesized answer. GEO builds on SEO rather than replacing it.
  - https://geo.wiki/zh/seo-vs-geo (中文)
- https://geo.wiki/sitemap-and-indexnow — Sitemap & IndexNow — Sitemap.xml is a pull protocol from 2005. Microsoft and Yandex introduced the push-based IndexNow protocol in 2021. Both affect AI visibility through host indexes: Google AI Overviews via Google and Bing Copilot via Bing. ChatGPT, Perplexity, and Claude use neither directly.
  - https://geo.wiki/zh/sitemap-and-indexnow (中文)
- https://geo.wiki/zero-click-search — Zero-click Search — A zero-click search ends on the results page without a visit to a source. Most Google searches already ended without a click before generative answers further increased zero-click behavior. For GEO, value shifts from earning a click to earning a citation or mention.
  - https://geo.wiki/zh/zero-click-search (中文)

## AI Engines (platform deep dives)

- https://geo.wiki/platforms/bing-copilot — Microsoft Bing Copilot — Copilot Search in Bing grounds its answers on Bing search results. Bing indexing determines whether a page enters the web candidate pool. Retrieval and citation selection determine whether it appears in a particular answer.
  - https://geo.wiki/zh/platforms/bing-copilot (中文)
- https://geo.wiki/platforms/chatgpt-search — ChatGPT Search — ChatGPT Search is retrieval-augmented chat: ChatGPT fetches the live web only when the model decides to, so citations are sparser than Perplexity's. Its edge is distribution scale. The load-bearing GEO fact: OAI-SearchBot — not GPTBot — controls Search visibility.
  - https://geo.wiki/zh/platforms/chatgpt-search (中文)
- https://geo.wiki/platforms/claude — Claude — Claude adds live web retrieval to its chat, Research, and API surfaces. Search is conditional, citations appear when web results support an answer, and Anthropic uses three separate agents for potential training collection, search indexing, and user-requested fetching.
  - https://geo.wiki/zh/platforms/claude (中文)
- https://geo.wiki/platforms/deepseek — DeepSeek — DeepSeek combines an official chat product with optional web search, open-weight models, and a separate API that now supports server-side search. Its crawler identity and ranking logic remain undisclosed, so consumer, API, and self-hosted results must be measured separately.
  - https://geo.wiki/zh/platforms/deepseek (中文)
- https://geo.wiki/platforms/google-ai-overviews — Google AI Overviews — Google AI Overviews is the SERP-embedded engine: an AI summary block atop classic Google Search, grounded on the normal web index via query fan-out. The load-bearing GEO fact — there is no AI Overviews crawler. Googlebot controls eligibility; Google-Extended does not.
  - https://geo.wiki/zh/platforms/google-ai-overviews (中文)
- https://geo.wiki/platforms/google-gemini — Google Gemini — Gemini is the Google-stack retrieval-augmented-chat engine: a chat model given a Grounding-with-Google-Search tool, conditional not SERP-embedded. It shares AI Overviews' Google index, but the GEO fact is inverted: Google-Extended is the control here.
  - https://geo.wiki/zh/platforms/google-gemini (中文)
- https://geo.wiki/platforms/perplexity-ai — Perplexity AI — Perplexity is an answer-engine-native generative engine: it retrieves the live web by default and ships every answer with numbered inline citations — the most citation-dense, transparent mainstream engine, and the live-engine baseline GEO research and field tests use most.
  - https://geo.wiki/zh/platforms/perplexity-ai (中文)

## Playbooks

- https://geo.wiki/playbooks/ai-citation-tracking — AI Citation Tracking — Use a repeatable manual and automated workflow to measure how often, how prominently, and on which AI engines your content is cited. Define the prompt set, sample answers, verify URLs, normalize the data, and report results using the definitions in GEO Metrics.
  - https://geo.wiki/zh/playbooks/ai-citation-tracking (中文)
- https://geo.wiki/playbooks/ai-crawler-access-audit — AI Crawler Access Audit — An AI crawler access audit compares four forms of evidence: intended policy, live declarations, actual responses, and verified arrivals. Differences between adjacent states reveal access failures, including network-level blocks that robots.txt cannot show.
  - https://geo.wiki/zh/playbooks/ai-crawler-access-audit (中文)
- https://geo.wiki/playbooks/brand-mention-tracking — Brand Mention Tracking — Track brand mentions in AI answers by running an alias-aware detector against a frozen prompt set, verifying each match, and calculating Mention Frequency, Share of Voice, Answer Inclusion Rate, and Brand Sentiment as defined in GEO Metrics.
  - https://geo.wiki/zh/playbooks/brand-mention-tracking (中文)
- https://geo.wiki/playbooks/citability — Citability Audit — A citability audit tests whether individual passages can stand alone when an AI system retrieves them for an answer. Start with a manual chunk-extraction test, assess seven signals, rate each finding by severity, and record the appropriate rewrite for each failure.
  - https://geo.wiki/zh/playbooks/citability (中文)
- https://geo.wiki/playbooks/geo-audit — Full GEO Audit — A full GEO audit reviews six dependent areas in order: access, rendering, structure, content, off-site authority, and observed outcomes. This playbook explains how to sequence the checks, decide when later checks are meaningful, assign severity, and produce an actionable report.
  - https://geo.wiki/zh/playbooks/geo-audit (中文)
- https://geo.wiki/playbooks/geo-maturity-model — GEO Maturity Model — Assess GEO capability across five dimensions and five levels: Unmanaged, Instrumented, Systematic, Competitive, and Reference. Your overall level is the lowest dimension score, not the average. Each level has an exit test, priority actions, relevant KPIs, and a common pitfall.
  - https://geo.wiki/zh/playbooks/geo-maturity-model (中文)
- https://geo.wiki/playbooks/llms-txt-deployment — Deploying llms.txt — Deploy llms.txt in four stages: curate the file, generate it from site data, and serve clean markdown for its links. No major vendor confirms consumption and no official validator exists, so limit the initial work to about a day and remove an unmaintained file.
  - https://geo.wiki/zh/playbooks/llms-txt-deployment (中文)
- https://geo.wiki/playbooks/schema-audit — Schema Audit — A schema audit tests two consumers of inherited markup with opposite failure modes: parsers silently discard invalid blocks, while live-fetch models still read them as page text. It checks coverage, validity, integrity, and truth; only truth failures carry a documented penalty.
  - https://geo.wiki/zh/playbooks/schema-audit (中文)
- https://geo.wiki/playbooks/schema-implementation — Schema Implementation — Deploy Schema.org in three tiers: entity identity first, page types second, and conditional markup for assets and answer formats last. Render it in the initial HTML, run four checks, and keep it consistent with the visible page. Expect easier parsing, not more citations.
  - https://geo.wiki/zh/playbooks/schema-implementation (中文)
- https://geo.wiki/playbooks/writing-for-ai-citation — Writing for AI Citation — Use seven signal-specific recipes to make passages easier for AI systems to cite. Each recipe includes a before-and-after example, and the playbook adds an MDX template, a pre-publish checklist, and common mistakes to avoid.
  - https://geo.wiki/zh/playbooks/writing-for-ai-citation (中文)

## Research papers

- https://geo.wiki/papers/aggarwal-geo-benchmark-2024 — GEO: Generative Engine Optimization (Aggarwal et al. 2024) — Aggarwal et al. coined Generative Engine Optimization and introduced GEO-bench and its impression metrics, reporting visibility gains of up to 40%. The maximum varied by method and domain and was about 22% on Perplexity.ai.
  - https://geo.wiki/zh/papers/aggarwal-geo-benchmark-2024 (中文)
- https://geo.wiki/papers/cseo-bench-2025 — C-SEO Bench: Does Conversational SEO Work? (Puerto et al. 2025) — C-SEO Bench found that most of the ten white-hat conversational SEO rewrites did not reliably improve citation rank and often made it worse. Retrieval order had a stronger effect, while gains declined as competitors adopted the same method.
  - https://geo.wiki/zh/papers/cseo-bench-2025 (中文)
- https://geo.wiki/papers/gao-rag-survey-2023 — Retrieval-Augmented Generation for Large Language Models: A Survey (Gao et al. 2023) — Gao et al. organize RAG into Naive, Advanced, and Modular paradigms, then examine retrieval, generation, augmentation, and evaluation. The survey explains how generative engines use external knowledge, but it does not show which GEO tactics improve visibility.
  - https://geo.wiki/zh/papers/gao-rag-survey-2023 (中文)
- https://geo.wiki/papers/what-evidence-convincing-2024 — What Evidence Do Language Models Find Convincing? (Wan et al. 2024) — In an ACL 2024 study of conflicting web sources, five LLMs favored topical relevance while giving little weight to scientific references, neutral tone, and formal citations. Every model tested predates mid-2024.
  - https://geo.wiki/zh/papers/what-evidence-convincing-2024 (中文)

## GEO product profiles

- https://geo.wiki/products/llmrefs — LLMrefs — LLMrefs is a keyword-first AI-search analytics platform for SEO teams, in-house marketers, and agencies that need recurring brand visibility, competitor, citation-source, and fan-out prompt evidence across multiple answer engines.
  - https://geo.wiki/zh/products/llmrefs (中文)
- https://geo.wiki/products/otterly-ai — Otterly.AI — Otterly.AI is an AI-search monitoring and optimization platform for marketing teams and agencies that tracks brand mentions, competitors, and cited URLs, then connects those observations to prompt research, GEO audits, recommendations, exports, and reporting integrations.
  - https://geo.wiki/zh/products/otterly-ai (中文)
- https://geo.wiki/products/peec-ai — Peec AI — Peec AI is an AI-search analytics platform for marketing teams and agencies, centered on prompt-level visibility, position, sentiment, citations, competitors, and prioritized actions.
  - https://geo.wiki/zh/products/peec-ai (中文)
- https://geo.wiki/products/profound — Profound — Profound is an AI-search marketing platform that combines answer-engine visibility, citation and prompt-demand analysis, crawler analytics, shopping monitoring, and autonomous marketing agents.
  - https://geo.wiki/zh/products/profound (中文)

## Meta

- https://geo.wiki/llms.txt — Site overview for LLMs
- https://geo.wiki/robots.txt — Crawler policy
- https://geo.wiki/sitemap-index.xml — Sitemap index