The definitive guide to LLMO (Large Language Model Optimization) — making your content discoverable by AI.
🌐 Website: llmoframework.com
LLMO is the practice of optimizing web content so that AI systems — including ChatGPT, Claude, Gemini, and Perplexity — can accurately discover, understand, and cite it.
LLMO includes approaches such as AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization).
The LLMO Framework defines five core components for AI discoverability:
- Knowledge Clarity — Is your content clear enough for AI to understand?
- Structural Formatting — Is your content structured for machine consumption?
- Retrieval Signals — Can AI systems find your content?
- Authority Signals — Does your content demonstrate expertise and trust?
- Citation Signals — Does your content provide references AI can verify?
This site follows the conventions of major framework documentation sites (Astro, React, Next.js, Tailwind CSS):
| Content Type | Location | Examples |
|---|---|---|
| Framework docs | /guide/, /framework/ |
Core concepts, component explanations |
| Implementation guides | /guide/ (docs section) |
"LLMO for WordPress", "LLMO for Next.js" |
| Case studies | /case-studies/ |
Production results with metrics |
| Research | /research/ |
Paper summaries, industry reports |
| Release notes | /blog/ (when needed) |
Framework updates, new research |
Key principles:
- Tutorials and how-to content go in docs, not blog
- Case studies go in a dedicated Showcase / Case Studies section
- Blog is reserved for release notes and announcements only
- All content must exist in all supported languages
| Language | Path | Status |
|---|---|---|
| English | / (root) |
Complete |
| Japanese | /ja/ |
Complete |
| Chinese | /zh/ |
Complete |
| Korean | /ko/ |
Complete |
| German | /de/ |
Complete |
| French | /fr/ |
Complete |
Translation contributions for additional languages are welcome.
We welcome contributions! Please see our contributing guide.
MIT
