embeddings Archives

A vector database stores embeddings and performs similarity search for semantic retrieval, recommendations, and retrieval-augmented generation.

Jul 18, 2026 by Daniel Mercer • 1 min read

Semantic search uses embeddings to retrieve information by meaning and context, finding relevant results even when documents use different words.

Jul 4, 2026 by Daniel Mercer • 1 min read

Feature extraction transforms raw input into informative measurements or representations that a machine learning model can use.

May 4, 2026 by Ethan Caldwell • 1 min read

By Ethan Caldwell Apr 16, 2026 • 1 min read

An encoder transforms raw input into a compact representation that preserves information useful for another model component or task.

By Daniel Mercer Apr 3, 2026 • 1 min read

Latent space is a learned numerical representation where an AI model organizes hidden features and relationships within images, text, or other data.

By Marcus Lee Mar 10, 2026 • 1 min read

Contrastive learning teaches a model useful representations by comparing examples that should be similar with examples that should differ.

By Marcus Lee Mar 2, 2026 • 1 min read

CLIP is a model approach that learns shared relationships between images and natural-language descriptions through contrastive training.

#Embeddings