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Plain-language AI glossary

Term 06TechniquesMeaning / context / connections

Techniques / Definition

Embeddings

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

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01

MeaningThe one-sentence definition.

02

ContextHow the idea works in practice.

03

UsesWhere the concept becomes useful.

01 / Plain-language context

How Embeddings 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).

02 / Practical uses

Where it helps.

  1. 01Semantic search
  2. 02Document similarity
  3. 03Recommendation systems
  4. 04Clustering and classification
03 / Keep exploring
RAG (Retrieval-Augmented Generation)A technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer.Vector DatabaseA specialized database optimized for storing and searching high-dimensional vector embeddings, enabling semantic similarity search.Semantic SearchA search approach that finds results based on meaning rather than exact keyword matches, using embeddings to understand the intent behind queries.