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Encyclopedia entries for GEO concepts, terms, standards, companies, people, and events.

Topic: Foundations (10) Signals (8) Infrastructure (7) Practice (1)

Foundations

  • AEO vs GEO
    Concept AEO vs GEO — the same construct in practice. AEO is the older umbrella term from the Featured-Snippet and voice-answer era of extractive single-passage answers; GEO is the term that stuck once generative synthesis became the default answer surface.
  • AI Search Timeline (2022–present)
    Concept AI search arrived sharply on 2022-11-30 with ChatGPT and has changed shape four times since: Shock (Bing Chat / Bard), Fragmentation (SGE / Claude / Perplexity, Aggarwal coined 'GEO'), Mainstream (AI Overviews / ChatGPT Search), Stabilization (AI Mode 1B MAU).
  • Answer Loop
    Concept A generative engine answers every query by running the same four-step runtime loop — query → retrieval → grounding → answer. GEO is not an abstract 'optimize the engine' move; it is intervening at each step, where each step has one lever you can push and one way you can fail.
  • Citation vs Mention vs Link
    Concept An AI answer credits you three non-equivalent ways: a citation (content credited with a reference), a mention (named, no link), or a link (a source, maybe not even used). Being grounded on and being credited are decoupled — each form maps to a different metric and lever.
  • Generative Engine
    Concept A generative engine answers a query by retrieving sources and synthesizing a written answer with an LLM, optionally crediting them. The break from a search engine is the output unit: returning documents vs. composing an answer — and it is the object GEO optimizes for.
  • Generative Engine Optimization
    Concept Generative Engine Optimization (GEO): getting your content retrieved, grounded on, and cited or mentioned in AI answers (ChatGPT, Perplexity, Google AI Overviews, Gemini). An extension of SEO, not a replacement — and GEO Wiki's front door to every GEO sub-topic.
  • GEO ROI Models
    Concept Under generative search the click stops attaching cleanly to value — and traditional ROI math breaks with it. Three currencies (citation, substituted traffic, brand authority) × three industry models (B2B SaaS, B2C e-commerce, media) is the frame this entry argues for.
  • LLMO vs GEO
    Concept LLMO vs GEO — mostly the same thing. 'LLM Optimization' shares GEO's goal and, in practice, near-identical trade definitions. 'Model layer' is a framing emphasis, not a separate discipline; the one genuinely distinct reading, training-corpus inclusion, is what GEO excludes.
  • SEO vs GEO
    Concept SEO vs GEO — same plumbing, different finish line. Both need crawlability, real expertise, clean structure and authoritative mentions; they split only at the success surface: a clicked rank vs a cited/mentioned answer. GEO is a layer on top of SEO, not a replacement.
  • Zero-click Search
    Concept The user gets the answer on the results page and never clicks a source. Predates AI — most Google searches were already zero-click before generative answers escalated it. The precondition for GEO: value moves from being clicked to being cited or mentioned.

Signals

  • AI Content Detection
    Concept An anti-signal entry — how AI engines down-weight content patterns associated with low-effort or scaled production, independent of whether AI tools were involved, and why classifier-based 'AI detection' is not the lever it is sold as.
  • Brand Mentions
    Concept An unlinked brand mention is a load-bearing GEO signal: being named across the web — with no link — feeds the model's entity prior and compounds into future answers. A mention is a different currency from a link, not a weaker one.
  • Citability
    Concept Citability is the structural property that decides whether a retrieved passage can be lifted, intact, into an AI-generated answer — independent of whether the source is trusted enough to be used at all. It is the shape half of grounding; E-E-A-T is the trust half.
  • E-E-A-T
    Concept E-E-A-T — Experience, Expertise, Authoritativeness, Trustworthiness — is Google's quality-rater framework, not an algorithmic score. In GEO it is the trust half of grounding: whether a retrieved, liftable passage's source is worth using. Orthogonal to citability, the shape half.
  • Entity Recognition
    Concept Entity recognition is the layer where an AI engine decides which known entity a name refers to. It is the join that attaches a mention, a citation, or a markup assertion to the right node — if you do not resolve, credit leaks, lands on the wrong entity, or is dropped.
  • Knowledge Graph Presence
    Concept Knowledge graph presence is having a structured node an AI engine already trusts — Wikipedia, Wikidata, the Google Knowledge Graph. It is an amplifier, not a cause: it lifts the model prior and gives resolution a destination, but cannot be self-declared into existence.
  • Multilingual GEO
    Concept Multilingual GEO is what changes when a query, page, or citation crosses a language boundary. The GEO loop's shape is invariant; four things vary — source pool, entity binding, chunk shape, trust pool. Per-language source-pool difference is the load-bearing fact.
  • Multimodal Signals
    Concept Multimodal signals are what AI engines read on non-text assets — images, video, audio, charts. The text channel (alt, caption, transcript, schema) still dominates over pixel vision in 2026 web-retrieval pipelines for both index-integrated and live-fetch AI.

Infrastructure

  • AI Crawlers
    Concept AI crawlers split into three categories with opposite access consequences — training, retrieval, and user-triggered. The decision is per-category, not per-bot; the costliest GEO mistake is blocking the citation category to stop the training one.
  • Core Web Vitals (LCP/INP/CLS)
    Concept Core Web Vitals (LCP / INP / CLS) is a Google ranking signal, not an AI-engine signal. The GEO effect is bounded — direct on Google AI Overviews, partial on Bing Copilot, negligible on ChatGPT Search, Perplexity, and Claude. AI crawler perf is a separate problem with its own fix.
  • JSON-LD
    Standard JSON-LD is one of three Schema.org serializations — Google recommends it because it lives in a script tag, doesn't touch visible HTML, and is easy to maintain. Index-integrated AI parses it as structured data; live-fetch chatbots read it as plain text on the page.
  • llms.txt
    Standard llms.txt is a proposed convention (Answer.AI, 2024): one curated markdown file at /llms.txt naming the pages an LLM should read first. Supply-side adoption is real; demand-side consumption is unconfirmed. A forward-compatible bet, not a citation channel.
  • robots.txt
    Standard robots.txt is RFC 9309 — a voluntary request, not access control. Compliant AI crawlers honor it as documented; non-compliant or spoofed ones do not. Write the policy per category (training / retrieval / user-triggered) and verify the rest at the network layer.
  • Schema.org for AI
    Concept Schema.org markup is not a ranking or citation signal. For AI it is infrastructure — it disambiguates who/what you are and makes a page parseable. Index-integrated AI uses it; live-fetch chatbots read JSON-LD as plain text. It makes an entity resolvable, not a passage liftable.
  • Sitemap & IndexNow
    Standard Sitemap.xml (2005, pull) and IndexNow (2021, push, Bing/Yandex only) are two submission protocols. Their AI effect transits only via host search indexes — AIO via Google, Bing Copilot via Bing. ChatGPT, Perplexity, and Claude consume neither directly.

Practice

  • GEO Metrics
    Concept The 10 core KPIs for measuring GEO — definitions, formulas, SEO equivalents, and how each major vendor (Profound, Otterly, Ahrefs, BrightEdge, Similarweb) actually defines them. A GEO Wiki synthesis, not an industry standard.