AI Search · 22 January 2026 · 7 min read
AEO vs GEO vs LLMO explained (without the hype)
Answer Engine Optimization, Generative Engine Optimization and Large Language Model Optimization overlap but solve different problems. Here is a clear breakdown with what to do for each.
Quick answer
AEO optimises for direct answers in featured snippets and assistants. GEO optimises for inclusion inside generative answers such as AI Overviews and Perplexity. LLMO optimises how language models represent your brand overall, including in answers with no live retrieval. Most brands need all three.
Side-by-side comparison
| AEO | GEO | LLMO | |
|---|---|---|---|
| Target surface | Snippets, voice, assistants | AI Overviews, Perplexity, Copilot | The model's internal brand representation |
| Core tactic | Question-answer formatting | Retrievable, citable passages | Entity consistency and corroboration |
| Main asset | FAQ and how-to content | Comparison and data pages | Off-site mentions and profiles |
| Success metric | Snippet capture rate | Citation rate per prompt | Accuracy of unprompted brand descriptions |
What AEO work looks like
Deliverables
- One clear question per H2, answered in the following 40-60 words
- FAQPage and HowTo schema on the pages that deserve it
- Definitions, specs and prices stated as plain sentences, not marketing copy
- Tables for anything comparative — machines extract tables reliably
What GEO work looks like
Deliverables
- Self-contained passages that make sense without the surrounding page
- Original data, benchmarks or pricing that no competitor can copy
- Explicit dates, sources and methodology so the answer can be verified
- Crawler access for AI user agents plus fast server responses
What LLMO work looks like
Deliverables
- Identical brand facts across your site, LinkedIn, Crunchbase, Wikidata and directories
- Organization schema with complete sameAs links
- Third-party mentions that repeat your positioning in the same words
- Regular audits asking models to describe your brand and correcting what they get wrong
Frequently asked questions
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