AI Glossarytechniques

Embeddings

Numerical vector representations of text that capture semantic meaning, enabling similarity search and clustering.

How It Works

Embeddings convert text into arrays of numbers (vectors) where similar meanings are close together in vector space. "King" and "Queen" would have similar embeddings, while "King" and "Banana" would be far apart. This is the foundation of RAG systems, semantic search, and recommendation engines. OpenAI's text-embedding-3-small produces 1536-dimensional vectors. You store these in vector databases like Pinecone, Weaviate, or pgvector (Supabase).

Common Use Cases

  • 1Semantic search
  • 2Document similarity
  • 3Recommendation systems
  • 4Clustering and classification

Related Terms

RAG (Retrieval-Augmented Generation)

A technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer.

Vector Database

A specialized database optimized for storing and searching high-dimensional vector embeddings, enabling semantic similarity search.

Semantic Search

A search approach that finds results based on meaning rather than exact keyword matches, using embeddings to understand the intent behind queries.

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