# GEO vs AEO vs LLMO — Definitions and Differences GEO, AEO and LLMO are three of the most-used acronyms for the same broad goal — getting your brand cited inside AI answers — but they are not strictly synonyms. Each came from a different moment in the field, each carries a different emphasis, and each gets used differently by practitioners, vendors and the academic literature. This is the working disambiguation, with sources, plus the one structural difference nobody else flags: China is a separate surface that none of these frameworks default to covering. ## One-table snapshot | Term | Stands for | Coined / popularised | Emphasis | Engines covered (default) | |---|---|---|---|---| | **AEO** | Answer Engine Optimization | ~2019, Featured-Snippets era; revived for AI in 2024 | Direct-answer questions; voice search; Featured Snippets-style extraction | Google AI Overview, Bing Copilot, ChatGPT (search mode), Perplexity | | **GEO** | Generative Engine Optimization | 2024 KDD paper, Aggarwal et al. (Princeton + IIT Delhi) | Citation selection and absorption inside generated answers; measurement-led | ChatGPT, Claude, Perplexity, Gemini, Google AI Overview, plus DeepSeek/Qwen/Doubao | | **LLMO** | LLM Optimization | Emerging 2025; no canonical paper yet | Optimising for the underlying language model rather than for any single product surface | The model layer (GPT-5, Claude 4, Gemini 2, etc.) — abstracts over the product UI | | **AI Visibility** | (umbrella term) | Industry-popularised by SaaS measurement firms | Brand-side metric: are we cited / mentioned / linked across AI surfaces? | Whatever the vendor measures — varies by tool | | **AI SEO** | (umbrella term) | Marketing-press shorthand | Loosely "SEO, but for AI engines" | Same as GEO in most usage | > **Practical answer.** If you have to pick one umbrella term, use **GEO**. It has the only peer-reviewed origin paper, the broadest engine coverage, and the cleanest measurement framework (selection vs absorption vs mention). AEO is the right term when the surface is specifically Featured-Snippet-style direct-answer extraction. LLMO is mostly used by vendors trying to differentiate from "GEO" branding but the underlying tactics overlap heavily. ## AEO — Answer Engine Optimization AEO predates the generative-AI era. The term was in use circa 2019–2022 to describe optimising for Google's Featured Snippets, "People Also Ask" boxes, and voice-assistant answers (Google Assistant, Alexa, Siri). The signal those surfaces shared: they pulled a single direct answer from a source page rather than ranking the page in a list. Optimising for that meant writing pages that answered a question in 40–60 words, with the answer in the first paragraph or a structured list. When ChatGPT search and Perplexity emerged, the AEO label was repurposed because the underlying mechanic was similar — extract a direct answer, cite or paraphrase the source. HubSpot's [AEO vs SEO explainer (2024)](https://blog.hubspot.com/marketing/aeo-vs-seo) is the most-linked piece in the modern revival, and Profound's [AEO definition](https://www.tryprofound.com/resources/articles/what-is-answer-engine-optimization) is the most-quoted vendor source. **What AEO emphasises in practice:** - Direct-answer-first paragraph structure (the "inverted pyramid"). - Question-format H2s that match real user phrasings. - Definition + 1–3-sentence summary in the first 200 words of every page. - Schema markup that flags answer chunks. FAQPage was once popular here but the published evidence on its citation lift is negative — see the FAQPage caveat in [our GEO definition](https://www.eastbound.ai/what-is-generative-engine-optimization/). **When to use the AEO label:** when you are optimising specifically for direct-answer surfaces (Google AI Overview, Bing Copilot answer boxes, ChatGPT in search mode answering a how-to question). When the page is a generative summary across many sources, AEO is the wrong frame and GEO is closer. Read more: [What is answer engine optimization?](https://www.eastbound.ai/what-is-aeo/) ## GEO — Generative Engine Optimization GEO has a single canonical origin: [Aggarwal et al., "GEO: Generative Engine Optimization" (KDD 2024)](https://arxiv.org/abs/2311.09735), a Princeton + IIT Delhi paper that ran a 10,000-query benchmark across nine optimisation tactics and reported the headline numbers most of the field now cites — adding authoritative citations lifted citation rate +115%, direct quotes +43%, named statistics +33%. [Wikipedia's GEO entry](https://en.wikipedia.org/wiki/Generative_engine_optimization) uses the KDD definition as its anchor. Search Engine Land's [2026 GEO mastery guide](https://searchengineland.com/mastering-generative-engine-optimization-in-2026-full-guide-469142) and Semrush's [GEO practical guide](https://www.semrush.com/blog/generative-engine-optimization/) both cite Aggarwal as the foundational source. **What GEO emphasises in practice:** the defining contribution is the two-stage measurement model — separating **citation selection** (does the engine retrieve your page into its source pool?) from **citation absorption** (does the page's language, structure or facts actually shape the answer the user reads?). Tw93's 2026 instrumentation of ChatGPT made the gap concrete: the engine retrieves roughly 100 pages per query, but only ~15% appear in the answer. This forces a different tactical hierarchy than AEO: 1. Crawlability and indexing (selection floor). 2. Content density and citation patterns (absorption — page-level language and evidence). 3. Off-site source-graph presence (selection lift — third-party citation is roughly **6.5× more effective** than self-citation alone in published samples). **When to use the GEO label:** when the engine is generating a synthesised answer across multiple sources rather than extracting a single one. ChatGPT, Claude, Perplexity (deep research mode), Gemini, and the three Chinese engines (DeepSeek, Qwen, Doubao) all default to generative behaviour for product-recommendation prompts. Read more: [What is generative engine optimization?](https://www.eastbound.ai/what-is-generative-engine-optimization/) ## LLMO — LLM Optimization LLMO is the youngest of the three. There is no canonical academic paper yet. The term emerged in vendor marketing in 2025 as a way to differentiate measurement tools that look at the underlying language model rather than at any single product surface. In practice, the LLMO label is often used interchangeably with GEO. [Neil Patel's 2025 explainer](https://neilpatel.com/blog/aeo-vs-geo-vs-llmo/) treats them as a "rolling rebrand of the same field." Profound's [AEO vs GEO post](https://www.tryprofound.com/blog/aeo-vs-geo) goes further and argues "AEO and GEO are the same thing." Our reading: LLMO has a small but legitimate distinct meaning when the work is genuinely model-layer (e.g., prompt-engineering test suites, training-data audits) and is mostly a marketing relabel otherwise. **What LLMO emphasises in practice:** - Prompt-panel testing across models (the same prompt at GPT-5, Claude 4, Gemini 2 — does brand recall persist?). - Training-data presence rather than retrieval-time presence. - Token-level and citation-pattern analysis — what content shape gets quoted verbatim? - Cross-vendor comparison rather than per-product optimisation. **When to use the LLMO label:** when the work is genuinely at the model layer. If the work is "we want to rank in ChatGPT's search-mode answers", that is GEO, not LLMO. Read more: [What is LLM optimization?](https://www.eastbound.ai/what-is-llmo/) ## "AI Visibility" — the umbrella term "AI visibility" is the broadest and least technical of the labels. It is mostly used by SaaS measurement vendors (Profound, AthenaHQ, Otterly.AI, Peec AI, Goodie, Mangools, Semrush AI Visibility, SE Ranking) to describe the brand-side metric of "how present is our brand across AI surfaces". Profound, the most-funded specialist in this space ($96M Series C at a roughly $1B valuation per their [Series C announcement](https://www.tryprofound.com/blog/profound-raises-96m-series-c)), markets primarily on the AI Visibility frame. **When to use the AI Visibility label:** when the audience is brand-side (CMOs, brand managers, marketing leadership) rather than practitioner-side. When the conversation is about reporting and measurement rather than tactical changes. Read more: [AI visibility vs SEO](https://www.eastbound.ai/ai-visibility-vs-seo/). ## Where the terms actually differ in practice Most commentary frames GEO/AEO/LLMO as interchangeable. They are mostly interchangeable, but there are three places the difference matters. **1. Measurement scope.** GEO measurement (per Aggarwal) measures citation lift in generated answers across 10,000 prompts. AEO measurement (legacy) measures appearance in Featured-Snippet-style direct answers. LLMO measurement (when done seriously) measures cross-model prompt-panel behaviour. AI Visibility measurement (the SaaS tool category) measures brand mentions, citations and sentiment across whatever the vendor crawls. **2. Tactical hierarchy.** AEO leans heavily on direct-answer-first paragraph structure and schema. GEO de-emphasises schema (per Williams-Cook's controlled tests, FAQPage and most JSON-LD has no measurable citation lift in non-Bing engines) and emphasises evidence density and third-party source-graph presence. LLMO emphasises prompt-panel testing rather than page-level changes. **3. Engine coverage.** AEO defaults to Google AI Overview + Bing Copilot. GEO defaults to ChatGPT, Claude, Perplexity, Gemini, AI Overview, plus the Chinese engines if the practitioner is paying attention. LLMO defaults to the model layer. None of them default to covering DeepSeek/Qwen/Doubao at the source-graph level. ## When to use which term — decision tree | If you are… | Use this term | Why | |---|---|---| | A brand or CMO talking to leadership about AI presence | **AI Visibility** | Vendor-neutral, measurement-framed, no tactical baggage | | An SEO practitioner doing on-page work for ChatGPT / Claude / Perplexity | **GEO** | Has the published research; selection-vs-absorption framework matches what these engines do | | An SEO practitioner targeting Google AI Overview specifically | **AEO** | AI Overview behaves like a Featured Snippet; AEO tactics map directly | | Running cross-model prompt panels or training-data audits | **LLMO** | The legitimate distinct meaning | | Working on Mainland-Chinese AI surfaces (DeepSeek, Qwen, Doubao) | **GEO China** or **China AI visibility** | The umbrella terms default to Western engines; China requires a different source-substrate model | | Writing a marketing brief for a non-technical audience | **AI Visibility** or **AI SEO** | Most recognisable; no acronym translation needed | ## China is a separate generative-search surface Every term covered above — GEO, AEO, LLMO, AI Visibility, AI SEO — was coined with Western engines in mind. The Aggarwal KDD paper benchmarks against Google AI Overview, Bing Copilot, Perplexity and ChatGPT. Profound, AthenaHQ, Otterly, Peec, Goodie, Mangools and SE Ranking all measure Western engines by default. None of them includes DeepSeek, Qwen or Doubao in their out-of-the-box configuration. That gap matters because the source ecosystems differ by 70–80% between Western and Chinese engines. In our 540-call panel (May 2026), top-15 source overlap (Jaccard) between any two Chinese engines was 0.20–0.30 — and overlap between Western and Chinese engines is lower still. A GEO playbook built for ChatGPT cannot be ported to DeepSeek without rebuilding the source-substrate model from scratch — different language, different prompt style, different community platforms (Zhihu not Reddit, Xiaohongshu not YouTube), different encyclopedic anchors (百度百科 not Wikipedia). This is why Eastbound's research and consultancy work focuses specifically on the Chinese surface. For the deeper treatment, see [China AI visibility for global brands](https://www.eastbound.ai/china-ai-visibility/), the per-engine playbooks ([DeepSeek SEO](https://www.eastbound.ai/deepseek-seo/), [Qwen optimization](https://www.eastbound.ai/qwen-optimization/), [Doubao optimization](https://www.eastbound.ai/doubao-optimization/)), and the [measurement methodology](https://www.eastbound.ai/methodology/). ## Related reading - [What is answer engine optimization?](https://www.eastbound.ai/what-is-aeo/) - [What is LLM optimization?](https://www.eastbound.ai/what-is-llmo/) - [What is generative engine optimization?](https://www.eastbound.ai/what-is-generative-engine-optimization/) - [AI visibility vs SEO](https://www.eastbound.ai/ai-visibility-vs-seo/) - [China AI visibility for global brands](https://www.eastbound.ai/china-ai-visibility/) - [DeepSeek vs Qwen vs Doubao: Why Brands Look Different](https://www.eastbound.ai/blog/three-chinese-ais.html) --- Run a free China AI visibility audit at https://www.eastbound.ai/ai-visibility-audit/ — DeepSeek, Qwen and Doubao on a stratified zh-CN consumer prompt panel.