Embeddings convert raw data — text, images, audio — into fixed-length numerical vectors that machine learning models can process. They're the universal interface between human-readable information and the mathematical operations at the heart of every modern AI system, from chatbots to image search.

Machine learning models operate on numbers, not words or images. Embeddings are the translation layer. Given a word like 'bank', an embedding model produces a vector like [0.23, -0.87, 0.14, ...] with hundreds of dimensions. The crucial property is that similar inputs produce similar vectors — 'bank' and 'finance' will be closer in vector space than 'bank' and 'cloud'. This geometric structure encodes semantic relationships that make downstream ML tasks dramatically more effective. Three types of embeddings are foundational to understand. Word embeddings (Word2Vec, GloVe) produce static vectors per word — the same vector for 'bank' regardless of context. Contextual embeddings (BERT, GPT) produce dynamic vectors that change based on surrounding words, capturing 'river bank' vs. 'investment bank' differently. Multimodal embeddings (CLIP, ImageBind) map text and images into the same vector space, enabling cross-modal search. Embeddings are not just for NLP: user embeddings power Netflix and Spotify recommendations; graph embeddings encode social network relationships; molecule embeddings accelerate drug discovery. They are the most versatile building block in modern AI, appearing in nearly every serious ML system in production today.

What Are Embeddings? The Fundamentals Explained