LLM Optimization
The practice of adapting content and digital presence to be better understood, indexed, and referenced by large language models.
LLM Optimization encompasses the strategies and tactics used to improve how large language models (LLMs) perceive, process, and present a brand's information. This includes ensuring content is well-structured, factually accurate, and widely cited across the web so that LLMs have high-quality training signals.
Key LLM optimization techniques include maintaining comprehensive and up-to-date entity information, using schema markup to provide structured context, earning mentions from authoritative sources, creating content that directly answers common questions, and ensuring consistency across all digital touchpoints.
LLM Optimization is broader than AEO in that it also considers how models are trained and fine-tuned, not just how they retrieve information at inference time.
Related Terms
Answer Engine Optimization (AEO)
The practice of optimizing content so that AI-powered search engines surface your brand in their generated answers.
Generative Engine Optimization (GEO)
A framework for optimizing content specifically for AI-powered generative search engines that synthesize answers from multiple sources.
Structured Data
Standardized code formats (like Schema.org markup) that help search engines and AI models understand the content and context of web pages.
AEO Vision Content Team
Insights on AI search visibility, answer engine optimization, and brand discovery across ChatGPT, Perplexity, Gemini, Claude, and Google AI Mode.
Track your LLM Optimization performance
AEO Vision helps brands measure and improve their AI search visibility across every major platform.