What is Vector Embedding?
A numeric representation of text meaning used to match queries to passages by semantic similarity rather than exact keywords. Embeddings power chunk retrieval in AI search.
Related terms
Cosine Similarity
A 0-to-1 measure of how alike two embeddings are, used to rank which passages answer a query. Higher similarity means the passage reads as more about the same thing as the question.
Semantic Similarity
How alike two texts are in meaning rather than shared keywords. Engines use it for deduplication, clustering, and retrieval; near-duplicate pages with different words still read as duplicates.
Chunking
Splitting content into retrievable passages for embeddings and RAG. Clear headings, one idea per section, and definitions up front make chunks that rank and quote cleanly.
Answer Engine Optimization (AEO)
Answer Engine Optimization is the practice of structuring pages so answer engines and AI assistants can extract, trust, and quote them: a direct definition up top, scannable steps, quotable facts, and FAQ or Article schema behind them.
Generative Engine Optimization (GEO)
Generative Engine Optimization improves how often a brand or page is cited inside AI-generated answers. It combines classic SEO (crawlability, relevance) with quotability: clear facts, named entities, sources, and machine-readable structure.
AI Overviews
Google’s AI-generated summaries at the top of some results pages. They synthesize multiple sources and link to them, which can reduce clicks to publishers even when a page ranks well organically.