AEO vs GEO vs LLMO — Which One Matters for Your Business?
Three acronyms, one goal: getting your business into AI-generated answers. Here's what AEO, GEO, and LLMO actually mean, how they differ, and which one you should focus on.
AEO (Answer Engine Optimisation) focuses on zero-click answers and featured snippets. GEO (Generative Engine Optimisation) covers citations in AI-generated answers across all platforms. LLMO (Large Language Model Optimisation) is the most technical, targeting specific model training and retrieval. For most businesses, GEO is the right umbrella term and starting point.
The big comparison table
| Dimension | AEO | GEO | LLMO |
|---|---|---|---|
| Full name | Answer Engine Optimisation | Generative Engine Optimisation | Large Language Model Optimisation |
| Focus | Featured snippets, zero-click answers | AI-generated answer citations | LLM training data and retrieval |
| Origin | Oldest term (pre-ChatGPT) | Broadest, most widely used | Most technical and specific |
| Targets | Google snippets, voice assistants, People Also Ask | ChatGPT, Gemini, Perplexity, AI Overviews, Copilot | GPT, Claude, Gemini, Llama at the model level |
| Technical depth | Low — mostly content formatting | Medium — content + technical SEO + entity work | High — schema, training data, RAG systems |
| Who uses the term | Traditional SEO agencies expanding scope | AI search specialists, newer agencies | Technical SEO teams, developers |
| Best for | Businesses already ranking in Google | Any business wanting AI visibility | Enterprise brands, SaaS, technical products |
AEO — Answer Engine Optimisation
AEO is the oldest of the three terms, predating the ChatGPT era. It originally referred to optimising for Google's featured snippets, voice assistant answers, and "People Also Ask" boxes. AEO is the narrowest definition — it focuses on getting your content selected as the direct answer to a question.
What AEO covers
- Google featured snippets (paragraph, list, table formats)
- Voice assistant responses (Google Assistant, Siri, Alexa)
- "People Also Ask" boxes
- Knowledge Panel information
- Zero-click search results
AEO tactics
- Question-and-answer content format
- FAQPage schema markup
- Concise answer paragraphs (40-60 words) after each heading
- List and table formatting
- Speakable schema for voice selection
Limitation of AEO
AEO was designed for a pre-generative era. It doesn't account for ChatGPT, Perplexity, or Gemini — platforms that generate multi-source synthesised answers rather than selecting a single snippet.
GEO — Generative Engine Optimisation
GEO is the broadest and most commonly used term. It covers optimisation for any AI system that generates answers by synthesising information from multiple sources. This includes ChatGPT, Gemini, Perplexity, AI Overviews, Copilot, and Claude. GEO is the umbrella term that most businesses should adopt.
What GEO covers
- ChatGPT citations (via Bing index)
- Gemini citations (via Google index)
- Perplexity citations (multi-source, real-time)
- AI Overviews citations (Google top 10)
- Microsoft Copilot citations (Bing-powered)
- Claude citations (training data)
GEO tactics
- Entity consistency across all platforms
- Schema markup (Organisation, Person, Article, FAQPage)
- Brand mention building (0.664 correlation with AI citations)
- Multi-index optimisation (Google + Bing)
- Website structure for AI crawlers
- Content freshness and regular updates
- Cross-platform visibility monitoring
Why GEO is the right default
GEO encompasses both AEO (snippets and voice) and the newer AI chat platforms. It's platform-agnostic, which matters because AI search market share shifts rapidly. Optimising for GEO means you're covered regardless of which platform gains dominance.
LLMO — Large Language Model Optimisation
LLMO is the most technical term, focused specifically on how large language models (GPT, Claude, Gemini, Llama) absorb and retrieve information. LLMO practitioners think about training data inclusion, retrieval-augmented generation (RAG), and model-level behaviour rather than search engine results pages.
What LLMO covers
- Training data optimisation — getting content included in model training
- RAG (Retrieval-Augmented Generation) — influencing real-time retrieval
- Embedding optimisation — how content is vectorised and stored
- Model-specific behaviour — different strategies for GPT vs Claude vs Gemini
- Crawler management — GPTBot, ClaudeBot, Google-Extended
LLMO tactics
- Allowing AI crawlers (GPTBot, ClaudeBot, Google-Extended) in robots.txt
- Creating llms.txt files for model consumption
- Factual density — specific claims the model can learn
- Entity embedding — consistent descriptions that models can vectorise
- Information gain — unique data that models haven't seen elsewhere
- Technical schema for machine readability
Which approach should your business take?
| Business type | Recommended approach | Why |
|---|---|---|
| Local service business | GEO | Needs visibility across ChatGPT, Gemini, AI Overviews. Entity consistency and GBP matter most. |
| Ecommerce | GEO + AEO | Product snippets still drive traffic. AI platforms now show product recommendations. |
| SaaS / technology | GEO + LLMO | Technical audience uses Claude and ChatGPT. Training data inclusion matters. |
| Professional services | GEO | Brand mentions and entity authority drive AI citations in this sector. |
| Enterprise / global brand | All three | Resources to pursue comprehensive strategy across snippets, AI platforms, and model training. |
| Content publisher | GEO + LLMO | Traffic from AI referrals. Perplexity Publishers programme. Training data licensing. |
| B2B | GEO | Decision-makers increasingly use AI for vendor research. B2B AI search agencies can help. |
The overlap between all three
In practice, the three approaches share most of their tactics. The Venn diagram overlap is approximately 80%. Every approach benefits from:
- Clean, structured HTML
- Schema markup
- Entity consistency
- Factual, data-rich content
- Brand mention building
- Multi-platform indexation
The differences lie at the edges: AEO emphasises snippet formatting, GEO emphasises cross-platform monitoring, and LLMO emphasises technical model-level work.
Our recommendation
Use "GEO" as your umbrella term. It's the broadest, most understood, and most future-proof. If you need to go deeper into model-level optimisation, add LLMO tactics. If you're already strong in Google snippets, your AEO foundation will carry over.
The worst approach is to argue about terminology while doing nothing. All three terms point to the same reality: AI is changing how people find businesses, and you need to be visible in those AI answers.
What to do next
Oliver Mackman
AI Search Analyst, SEOCompare
Oliver leads SEOCompare's editorial and comparison research. With over a decade in digital marketing, he oversees agency evaluation, tool testing, and AI search data analysis.
Last reviewed: 7 April 2026
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