Definition

An embedding is a mathematical representation of data—like words, phrases, or images—converted into vectors (lists of numbers). These vectors help AI understand relationships based on meaning, not just keywords or surface features.

Example

“‘Dog’ and ‘puppy’ have similar embeddings, meaning AI sees them as closely related in meaning.”

How It’s Used in AI

Embeddings are the backbone of search, semantic search, recommendation engines, vector databases, and tools like RAG. They allow AI to find similar concepts, group related items, or reason across different types of content.

Brief History

Embeddings became popular with models like Word2Vec and GloVe. Now, transformer models like BERT and GPT create contextual embeddings that capture deeper meanings.

Key Tools or Models

• OpenAI Embeddings, Sentence-BERT, CLIP, and GloVe

• Used with tools like Pinecone, Weaviate, and Chroma for vector search

• Foundational in AI search and retrieval systems

Pro Tip

Not all embeddings are equal. Choose your embedding model based on your use case—images, text, or mixed data need different types.