Contextual Hierarchy (H1-H2-H3)

Contextual Hierarchy is a heading structure that builds semantic context for AI Search. The H1 defines the topic by combining Central Entity, UNIQUE attribute, and Source Context. H2 headings split content into sections that match sub-queries from fan-out, with each H2 representing one RAG chunk of 200–500 words. H3 headings add detail within sections. The order of H2 headings follows URR attribute classification. ROOT attributes appear first since they're most frequent in SERP results. The sequence follows: GAP P1, GAP P2, lower ROOT attributes, and finally FAQ sections containing RARE attributes and GAP P3–P4.

In practice, the heading hierarchy is built from competitive gap analysis data rather than keyword research tools. URR and gap priorities determine the structure. Contextual Hierarchy works with hypernyms to establish the H1→H2→H3 taxonomy. This hierarchy directly determines chunk boundaries for RAG processing.