LLM Optimized Headings

Extended definition

LLM Optimized Headings replace traditional keyword-focused headers with natural question formats that mirror how people interact with AI systems. Instead of 'Pricing Tiers,' use 'How much does [product] cost?' Instead of 'Implementation,' use 'How do you implement [solution]?' These headings match the conversational patterns in AI queries, making your content more likely to be retrieved when models search for answers. They also provide clear semantic signals about what each section addresses, helping AI systems extract the right information for specific question types.

Why this matters for AI search visibility

AI models retrieve content by semantic matching between user questions and document structure. When your headings directly answer the questions users ask, you increase retrieval probability dramatically. This optimization is especially critical as AI search grows—users ask questions, not keywords. Content with LLM-optimized headings appears more relevant to AI ranking systems and more useful to models extracting information. For high-value commercial queries, this structural optimization often determines whether you're cited or invisible.

Practical examples

• A product page restructured with question-based headings sees citation rates increase from 7% to 34% across AI engines
• A technical documentation site using 'How do I...' headings becomes the primary source in ChatGPT for implementation questions
• An about page with 'Who founded [company]?' and 'What does [company] do?' headings achieves 92% entity recognition accuracy