LLM Optimization
Also called LLMO.
LLM Optimization is the practice of structuring content and brand signals so Large Language Models — ChatGPT, Claude, Gemini, and the engines they power — preferentially cite and reference your content when generating answers.
What it means
LLM Optimization (LLMO) sits inside the broader GEO/AEO umbrella but emphasizes the specific characteristics of Large Language Models: they're trained on snapshots of web content, they retrieve information at inference time, they cite sources to varying degrees, and they preferentially trust certain types of structural and authority signals.
The optimization work breaks into two timeframes. Training-time optimization (your content has to be in the data when models are trained) requires brand mentions across high-authority sources LLMs scrape. Inference-time optimization (your content needs to surface during real-time retrieval) requires the same elements as classic SEO — but with structural emphasis on machine-readability.
Practical LLMO work includes: ensuring your brand is referenced in Wikipedia/Wikidata where merited, maintaining strong entity signals via structured data, publishing on platforms LLMs scrape heavily (Reddit, Stack Overflow, GitHub, major publications), and structuring content for easy extraction (clear headings, FAQ schema, definitional language).
Key takeaways
- Training-time and inference-time optimization are different problems
- LLMs cite sources differently — Perplexity prominently, ChatGPT sparsely, Gemini varies
- Brand mentions on Reddit, Wikipedia, and major news sites carry disproportionate weight
- Schema markup helps LLMs disambiguate entities and extract structured facts
- LLMO
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