LLMO (commonly known as GEO — Generative Engine Optimization — or LLM SEO) is the practice of structuring and crafting digital content so that large language models (LLMs) can accurately understand, interpret, and cite it in generated responses.
Overview
LLMO builds on traditional content strategy by focusing on semantic clarity, structure, and entity robustness rather than mere ranking mechanics. While SEO helps sites rank, LLMO ensures content is interpretable and reusable by AI systems in formats like Google AI Overviews, ChatGPT answers, or conversational search responses
It aligns with generative AI trends where visibility means being cited, not just found
Examples in Marketing & Design Contexts
-
In web design, labeling and structuring informational modules helps AI models interpret on-page sections—improving the odds your design insights get cited.
-
For SEO strategy, writing clear definitions and using schema ensures your content is more likely to be surfaced in AI-generated summaries.
-
In PPC, creating precise ad metadata with entity-rich copy increases the chance that AI-powered tools suggest your products when drafting ads or search recommendations.
-
On LLMO-driven content, designing modular FAQs or how-tos arranged by intent ensures AI assistants can use your answers directly.
Related Terms
-
[SEO] — traditional strategies to improve search ranking
-
[GEO] — generative engine optimization (often used interchangeably)
-
[AEO] — answer engine optimization for direct Q&A visibility
-
[Knowledge Graph] — entity-based content structuring that supports LLMO