Vector Embedding
A vector embedding is a numeric representation of text that captures its meaning as a point in high-dimensional space. Search and AI systems use embeddings to find content that is semantically similar to a query, not just keyword-matched.
Why It Matters
Answer engines and semantic search retrieve information by comparing embeddings, so content that clearly expresses concepts and entities is easier to match and cite. Writing for meaning, not just keywords, improves retrieval.
Common Mistake
Optimizing only for exact keywords. Embedding-based retrieval rewards clear, well-structured explanations of a topic, so keyword stuffing adds no value and can reduce clarity.