Dense numerical representations of text, images, or other data that capture semantic meaning in a high-dimensional vector space. Embeddings allow AI systems to measure similarity between concepts, retrieve relevant information based on meaning rather than keywords, and power semantic search, recommendation systems, and RAG pipelines — forming the mathematical foundation of modern AI understanding.
Related Terms
RAG (Retrieval-Augmented Generation)
A technique that enhances LLM responses by retrieving relevant information from external knowledge bases before generating an answer. RAG grounds AI responses in real, up-to-date data — reducing hallucinations and enabling agents to answer questions about proprietary or domain-specific content.
Semantic Memory
A knowledge storage system that allows AI agents to remember and recall information based on meaning rather than exact keywords. Semantic memory uses vector embeddings to store and retrieve contextually relevant information, enabling agents to maintain long-term context across conversations and channels.
Vector Database
A specialized database designed to store, index, and query high-dimensional vector embeddings at scale. Vector databases enable fast similarity search — finding the most semantically relevant documents, images, or data points based on meaning rather than exact keyword matches. They are essential infrastructure for RAG systems, semantic memory, and AI-powered search.