Technique

Embeddings

Embeddings are numerical representations of text, images, or other data that capture semantic meaning — enabling AI systems to understand similarity, search, and retrieve relevant information.

What Are Embeddings?

Embeddings are dense numerical vectors that represent the meaning of text, images, or other data in a mathematical space where similar items are close together. They are the foundation of semantic search, RAG systems, recommendation engines, and clustering algorithms.

Why Do Embeddings Matter for Business AI?

Embeddings enable AI to understand meaning, not just keywords. When a customer asks "I need to return this broken item," embeddings help the AI understand this is about "product returns" and "warranty claims" even if those exact words aren't used. Every business deploying RAG-based AI systems relies on embeddings stored in vector databases.

embeddingsvector representationssemantic AI

Related Terms

Vector Database

A vector database is a specialized database optimized for storing and querying high-dimensional embeddings, enabling fast similarity search that powers RAG, recommendations, and semantic search.

Vector Search

Vector search finds similar items by comparing their mathematical representations (embeddings) rather than matching keywords, enabling AI systems to understand meaning and context.

Semantic Search

Semantic search understands the meaning and intent behind queries rather than just matching keywords, using AI to deliver more relevant results from documents, databases, and knowledge bases.

RAG (Retrieval-Augmented Generation)

RAG is a technique that enhances AI responses by retrieving relevant information from external knowledge bases before generating an answer, combining the power of search with generative AI.

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