Understanding LLMO (Large Language Model Optimization)
Large Language Model Optimization (LLMO) is the most technical layer of AI discoverability, focusing specifically on how LLMs crawl, ingest, and represent your content in their training data and retrieval systems.
The llms.txt Standard
Just as robots.txt tells search engines what to crawl, llms.txt tells AI models how to understand your site. This file provides:
- Site Context: What your business does and your expertise areas
- Content Hierarchy: Which pages are authoritative on which topics
- Entity Relationships: How your content connects to broader knowledge graphs
- Update Frequency: How often AI systems should re-crawl
LLMO vs GEO vs AEO
While these terms overlap, they target different aspects:
- AEO: Featured snippets in traditional search
- GEO: AI citations in responses to user queries
- LLMO: How LLMs understand and index your content at the model level
LLMO is the infrastructure layer—without it, GEO and AEO optimizations may not reach AI systems effectively.
AI Crawler Permissions
Key AI crawlers to optimize for:
- GPTBot: OpenAI's crawler for ChatGPT
- ClaudeBot: Anthropic's crawler
- PerplexityBot: Perplexity's answer engine crawler
- GoogleOther: Google's AI training crawler
Key Takeaways
- llms.txt is the robots.txt equivalent for AI models
- LLMO focuses on the infrastructure layer of AI discovery
- AI crawlers (GPTBot, ClaudeBot) need explicit permissions
- Content hierarchies help LLMs understand authority
- LLMO complements GEO—both are necessary