SEO vs GEO vs AEO vs LLMO: What's the Difference?

The search optimization landscape has fractured into multiple frameworks, creating massive confusion for marketers. A Digiday article captured the sentiment perfectly: "WTF are GEO and AEO?" Meanwhile, Reddit's r/SEO_for_AI and the Am I Cited community are filled with debates about which framework matters most.

Here's the reality: these frameworks aren't competing alternatives — they're complementary layers of a modern search visibility strategy. Each addresses a different aspect of how people discover information in 2026.

Think of them as nested circles, with SEO as the foundation and LLMO as the most comprehensive layer.

Search Engine Optimization remains the bedrock of online visibility. Google processes over 8.5 billion searches daily, and organic search drives 53% of all website traffic according to BrightEdge data.

Core Focus: Ranking in Google's organic search results (the ten blue links) Key Tactics: Keyword optimization, backlink building, technical optimization, content quality, site speed Platforms: Google, Bing, Yahoo Metrics: Rankings, organic traffic, click-through rates, domain authority

SEO isn't going away. Google AI Overviews pull from the same index as organic results, so strong SEO fundamentals directly support AI visibility. But SEO alone is no longer sufficient — you need the additional layers of GEO, AEO, and LLMO to capture the full spectrum of search behavior.

Generative Engine Optimization targets the AI-generated responses that increasingly dominate search. When you ask Perplexity a question or see a Google AI Overview, the cited sources earned their placement through GEO.

Core Focus: Earning citations in AI-generated search responses Key Tactics: Comprehensive schema markup (14+ types), content clusters, citation-optimized formatting, E-E-A-T signals Platforms: Google AI Overviews, Perplexity, ChatGPT Browse, Bing Copilot Metrics: AI citations, AI referral traffic, rich result appearances

GEO is the fastest-growing optimization discipline. AI referral traffic grew 527% in 2025, and 47% of brands still lack a deliberate GEO strategy — creating significant opportunity for early movers. The Georgia Tech research paper on GEO found that specific strategies (adding statistics, citations, and quotations) increased AI search visibility by up to 40%.

Answer Engine Optimization is the most format-focused framework. It's about structuring your content so any answer-serving platform — from Google Featured Snippets to Alexa to ChatGPT — can easily extract and deliver your information.

Core Focus: Appearing as direct answers across search, voice, and AI platforms Key Tactics: Question-answer formatting, FAQ schema, concise answer capsules, voice-friendly content, structured data Platforms: Google Featured Snippets, voice assistants (Siri, Alexa), AI chatbots, People Also Ask boxes Metrics: Featured snippet wins, voice search placement, answer box appearances

AEO has the longest history of the three AI-adjacent frameworks. Originally focused on featured snippets and voice search, it has naturally expanded to encompass AI-generated answers. The principles remain the same: content formatted as clear, direct answers gets selected.

Large Language Model Optimization is the broadest framework. Rather than targeting specific platforms or formats, LLMO focuses on the AI models themselves — ensuring your content is prominent in their training data, accurately represented in their knowledge systems, and prioritized in their retrieval processes.

Core Focus: Making content recognizable and citable by LLMs across all applications Key Tactics: llms.txt implementation, entity authority building, clean semantic HTML, cross-platform consistency, authoritative backlinks Platforms: All LLM-powered applications (ChatGPT, Claude, Gemini, Copilot, and future models) Metrics: Entity recognition accuracy, AI citation frequency, training data representation

LLMO is the most forward-looking framework because it targets the underlying technology that powers all AI search platforms. As new AI applications emerge, strong LLMO fundamentals ensure your content is visible regardless of which platform users choose.

The most effective strategy combines all four frameworks in layers:

Layer 1: SEO Foundation — Get the basics right. Technical optimization, quality content, strategic backlinks, keyword research.

Layer 2: AEO Formatting — Structure content as answers. Use question headings, answer capsules, FAQ schema, and concise direct responses.

Layer 3: GEO Authority — Build citation-worthy content. Implement 8-14+ schema types per page, build content clusters, establish topical authority.

Layer 4: LLMO Presence — Maximize model-level visibility. Create llms.txt files, ensure entity consistency, build authoritative backlink profiles.

RankRocket implements all four layers automatically in every page network we build. With 14+ JSON-LD schema types, answer capsules, content cluster architecture, and AI-optimized structure, every RankRocket page is built for comprehensive search visibility across both traditional and AI search platforms.

The bottom line: Don't pick one framework. Implement all four. The businesses that capture visibility across traditional search AND AI search will dominate their markets in 2026 and beyond.