Glossary
Vector Search
Vector search retrieves content by comparing embeddings, matching passages on meaning rather than exact wording. It lets an engine surface a relevant passage even when the buyer's phrasing shares no keywords with the page, which is why AI answers often quote content that never targeted the query as written.
In plain terms
The engine looks for pages that mean the same thing, not pages that use the same words.
Why it matters
Keyword lists stop being a plan. Coverage of a genuine concept — clearly explained, in one place — is what makes a page findable across the long tail of phrasings buyers actually use.
How to apply it
- Cover a concept completely on one page instead of splitting it across near-duplicates.
- Write plainly; jargon-dense text matches fewer real questions.
- Consolidate overlapping pages so retrieval is not split between them.
Example
A benefits-administration company's glossary page on "COBRA continuation coverage" gets surfaced by an AI engine in response to the differently worded query "can I keep my health insurance after I quit my job", because vector search matched the two on meaning rather than shared keywords.
Related terms
Back to the full glossary (75 terms).
Ranking is no longer enough
You need to be cited, mentioned, and recommended.
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