Contextual Vector

Contextual Vector is a contextual vector defining three optimization dimensions for AI Search, combining Frame Semantics, Contextual Hierarchy, BLUF, and autonomous chunks into one coherent system. Three components: Contextual Hierarchy (macro: heading structure H1→H2→H3), Contextual Connections (macro: internal and external links building relationship networks), Contextual Structure (micro: BLUF in the first 50 words of each H2 section). Building a Contextual Vector follows four steps: (1) define Central Entity + Source Context, (2) build semantic frame = map of sub-queries, (3) convert frame elements into H2 headings = chunks for retrieval, (4) add BLUF to each H2.

Contextual Vector is an architectural plan of a house — the frame is a list of rooms, hierarchy is floors, BLUF is the entrance door to each room, and each room (chunk) must be functional on its own. In practice, treat Contextual Vector as a checklist before publication — if any of the three dimensions is weak, the article won't achieve its full citation potential.