AI-Focused Heading Hierarchy is the practice of designing your content’s heading structure to align with how AI systems parse, chunk, and semantically prioritise information. In traditional SEO, headings serve human readability and keyword emphasis. In LLM Optimisation (LLMO), headings become retrieval anchors that guide AI models toward contextually correct answers.
A well-structured hierarchy helps LLMs:
- Identify where one idea ends and another begins.
- Map questions to relevant answers within a page.
- Interpret semantic relationships between sections (e.g., methods vs. benefits).
Best practices include:
- Use H1 for page-level summaries with entity-rich phrasing.
- Use H2 for distinct, retrievable answer blocks.
- Use H3 and H4 for support layers: use cases, examples, lists.
- Match headings to likely user queries (“How does X work?”, “Why use Y?”).
Example: A LangSync methodology guide uses H2 headings like “What Is Answer Span Highlighting?” and “When Should You Use It?” Each section starts with a 2-sentence summary, making them prime candidates for AI extraction.
Headings also support in-document navigation for vector databases and enable partial retrieval for AI-overviews and SGE-style summarizers.
In AI-first design, headings aren’t just aesthetic; they’re a schema.
