Vector Embedding
A vector embedding is a numerical representation of text or content that captures semantic meaning so AI can understand relationships and information.
What is a vector embedding?
A vector embedding is a numerical representation of text, images, or other data that captures its semantic meaning in a multi-dimensional space. AI models convert words, phrases, or entire documents into arrays of numbers (vectors) where semantically similar content is positioned close together, enabling machines to understand relationships and meaning rather than just matching exact text.
Vector embeddings enable several key capabilities:
- Convert text and content into numerical representations that capture semantic meaning
- Position semantically similar content close together in a multi-dimensional space
- Power semantic search, recommendation systems, and AI-driven content discovery
- Allow AI to understand concept relationships and recognize synonyms beyond exact keyword matches
This technology enables search engines and LLMs to understand that "SEO expert" and "search engine optimization specialist" are semantically similar, improving search relevance and AI response accuracy.
Learn more: Explore how AI technologies like vector embeddings are transforming search in our CMO’s Guide to Content Strategy in the AI Era.
