What are embeddings?

Embeddings are numerical representations of data (text, images, etc.) in a high-dimensional vector space that capture semantic meaning.

What are embeddings?

In the context of AI and machine learning, embeddings are numerical representations of data — such as words, sentences, or images — in a continuous vector space. They capture semantic meaning, so items with similar meanings are positioned close together in the vector space.

How do embeddings work?

AI models convert input data into vectors (arrays of numbers) with hundreds or thousands of dimensions. These vectors encode the semantic properties of the input, enabling mathematical operations on meaning — for example, comparing how similar two pieces of text are.

Use cases for embeddings

Embeddings power semantic search, recommendation systems, content classification, anomaly detection, and retrieval-augmented generation (RAG) for AI applications.