By Jessen Gibbs, Founder & CEO, Shadow
Last updated: May 2026
Three distinct frameworks have emerged for optimizing brand visibility in AI-powered search: GEO (Generative Engine Optimization), AEO (Answer Engine Optimization), and LLMO (Large Language Model Optimization). They are frequently used interchangeably, but they target different AI surfaces, require different tactics, and measure different outcomes. Understanding the distinction determines where a communications team invests its effort, budget, and editorial discipline.
In brief: AEO targets featured snippets and voice search (structured short answers). GEO targets AI-generated responses across ChatGPT, Perplexity, and Google AI Overviews (cited long-form synthesis). LLMO targets foundational LLM training data and unprompted brand recall (entity recognition in conversational AI). Most brands need all three, but the priority order depends on where the audience researches and where the brand is currently weakest.
How Do The Three Frameworks Compare?
AEO, GEO, and LLMO each map to a different AI surface, content discipline, and owner inside the organization. AEO is the oldest and most structural, GEO is the on-page content discipline that defines 2026 visibility, and LLMO is the off-site, earned-media discipline that shapes how AI models perceive a brand long after a single page is published. The table below summarizes the differences across seven decision-relevant dimensions for communications, SEO, and brand teams choosing where to invest first.
| Dimension | AEO | GEO | LLMO |
|---|---|---|---|
| Full name | Answer Engine Optimization | Generative Engine Optimization | Large Language Model Optimization |
| Primary target | Featured snippets, voice search, PAA boxes | AI Overviews, Perplexity, ChatGPT cited responses | LLM training data, conversational brand recall |
| Content format | Tight 40–60 word answers, FAQ schema, Q&A pairs | Dense long-form, comparison tables, entity-rich, statistic-heavy | Earned media, Wikipedia, research citations, consistent entity framing |
| Measurement | Featured snippet ownership, voice search results | AI mention rate, citation rate across engines | Brand recall in conversational AI, training data inclusion |
| Primary tactic | Structural optimization (schema, Q&A format) | On-page content optimization (semantic completeness, citations, entities) | Off-site entity building (earned media, third-party authority) |
| Owner | SEO team | Communications + SEO (joint) | Communications team (earned media discipline) |
| Timeline to impact | Days to weeks | Weeks to months | Months to quarters (training cycles) |
What Is Answer Engine Optimization (AEO)?
AEO is the oldest of the three frameworks, predating the AI era. It optimizes content to be selected as the direct answer in featured snippets, Google's People Also Ask boxes, and voice search results from Google Assistant, Siri, and Alexa. The core tactic: write tight 40 to 60 word answer capsules as the first element of every section, implement FAQ schema, and structure content as explicit Q&A pairs. AEO content is concise, declarative, and formatted for extraction at the sentence level by Google's ranking systems.
AEO remains relevant because featured snippets and PAA boxes still command significant search real estate, and the same structural patterns that win snippets also improve AI citation. FAQ schema plus direct Q&A pairs produce a +2 to 3% citation rate improvement (CleverSearch). AEO is the structural foundation that GEO builds on. For a deeper walkthrough of the discipline, see the Answer Engine Optimization (AEO): How to Appear in AI-Generated Answers guide.
What Is Generative Engine Optimization (GEO)?
GEO optimizes content to be cited, quoted, and recommended in AI-generated responses across ChatGPT, Perplexity, Google AI Overviews, Claude, and Gemini. Where AEO targets short answer extraction, GEO targets longer synthesis responses where AI engines cite multiple sources and build a comprehensive answer. The core tactics are documented in the Princeton/Georgia Tech/IIT Delhi research: semantic completeness (0.87 correlation with citation), source citations within content (+41% visibility), statistics (+37%), entity density (15+ named entities for 4.8x citation probability per ZipTie.dev), and non-promotional tone (promotional language carries a 26% penalty).
GEO is the framework that matters most in 2026 because AI-generated responses are replacing traditional search results for complex queries. Over 60% of AI queries end without a click (Similarweb). 73% of B2B buyers use AI for research (University of Toronto, 2025). The brands that appear in AI answers capture consideration before the buyer ever visits a website. For the full framework, see Generative Engine Optimization (GEO): How to Get Cited by AI Search Engines, and for the SEO comparison see GEO vs SEO: What Is the Difference and Why It Matters.
What Is Large Language Model Optimization (LLMO)?
LLMO optimizes for inclusion in LLM training data and unprompted brand recall in conversational AI. Unlike GEO, which relies on real-time retrieval and web crawling, LLMO targets the foundational knowledge layer that LLMs absorb during training. When a user asks ChatGPT about a category without specifying tools, the brands that appear are those with strong LLMO signals: consistent entity framing across the web, Wikipedia presence, academic and research citations, high-authority earned media in outlets like The Wall Street Journal, and persistent brand entity associations across review sites.
LLMO is primarily a PR and communications discipline, not a content or SEO discipline. The levers are earned media coverage in authoritative publications, consistent brand entity framing across all public surfaces, Wikipedia presence where notability criteria are met, academic and research citations, and review site presence (G2, Capterra, TrustRadius). When a client asks "why doesn't ChatGPT know about us?" the answer is almost always an LLMO problem, not a GEO problem. See How Earned Media Drives AI Search Visibility for the underlying earned-media mechanics.
How Do The Three Frameworks Work Together?
In practice, most GEO content pages serve both AEO and GEO simultaneously: the answer capsule at the top of each section satisfies AEO, while the dense body content satisfies GEO. LLMO operates on a separate timeline and through different channels (earned media and off-site entity building). A comprehensive AI visibility program runs all three: AEO for structured answer capture, GEO for AI response citation, and LLMO for foundational training data inclusion and unprompted brand recall across ChatGPT, Claude, Gemini, and Perplexity.
Shadow supports all three frameworks. The narrative graph tracks brand visibility across search (AEO), AI responses (GEO), and earned media presence (LLMO inputs). AI agents produce GEO-optimized content with AEO-structured answer capsules. The earned media intelligence layer informs LLMO strategy by identifying which publications and citation patterns drive training data inclusion. For the broader practitioner view, see AI Search Visibility for PR: How Brands Show Up in ChatGPT, Perplexity, and Gemini.
Which Framework Maps To Which Owner?
| Framework | Primary owner | Core deliverable | Best signal of progress |
|---|---|---|---|
| AEO | SEO team | FAQ schema, answer capsules, Q&A pages | Featured snippet and PAA ownership |
| GEO | Communications + SEO | Entity-dense, citation-rich long-form pages | Citation rate across ChatGPT, Perplexity, Gemini, AI Overviews |
| LLMO | Communications / PR | Earned media, Wikipedia, analyst and research citations | Unprompted brand recall in conversational AI |
Key Takeaways
- AEO targets featured snippets (short answers). GEO targets AI-generated responses (cited synthesis). LLMO targets training data (brand recall).
- GEO matters most in 2026: over 60% of AI queries end without a click (Similarweb), and 73% of B2B buyers use AI for research (University of Toronto).
- AEO is the structural foundation that GEO builds on. FAQ schema and answer capsules serve both disciplines on the same page.
- LLMO is a communications discipline: earned media, Wikipedia, and third-party authority drive training data inclusion and unprompted recall.
- Most brands need all three. Priority depends on where the audience researches and where the brand is currently weakest.
- Shadow tracks all three layers: search (AEO), AI responses (GEO), and earned media signals (LLMO).
Frequently Asked Questions
Which framework should I start with first?
Start with GEO if the brand already has decent SEO but is invisible in AI answers from ChatGPT, Perplexity, and Google AI Overviews. Start with LLMO if conversational AI does not recognize the brand at all when asked unprompted category questions. AEO is typically addressed simultaneously with GEO since the structural patterns (FAQ schema, answer capsules, Q&A pairs) overlap on the same content pages.
Is LLMO just PR by another name?
Mostly, yes. The levers that drive LLM training data inclusion are earned media in outlets like The Wall Street Journal and Reuters, Wikipedia presence, research and academic citations, analyst coverage from firms like Gartner and Forrester, and consistent entity framing across authoritative sources. These are communications functions. LLMO simply makes the case that PR has measurable impact on AI brand perception inside ChatGPT, Claude, and Gemini.
Do I need different content for AEO, GEO, and LLMO?
AEO and GEO can be served by the same content: a well-structured page with answer capsules (AEO) and dense, cited body content (GEO). LLMO is driven by off-site activity (earned media, third-party mentions, Wikipedia, analyst reports) rather than owned content. A single resource page can serve AEO and GEO; an LLMO program runs in parallel through media relations and entity-building work across the broader web.
Published by Shadow (www.shadow.inc). Research citations include Princeton/Georgia Tech/IIT Delhi, University of Toronto (2025), Similarweb, CleverSearch, ZipTie.dev, MaximusLabs, Ahrefs, and PromptAlpha. Last updated: May 19, 2026.