A list of numbers that captures the meaning of a word, a sentence or an image.
Computers only handle numbers, so AI turns words and sentences into lists of hundreds or thousands of numbers. That list is called an embedding, or a vector.
The numbers aren't random. They're learned so that words used in similar ways get similar numbers, which puts similar meanings close together. A computer can then tell that "lunch" and "midday meal" are close, even though they share no words. Google's word2vec, released in 2013, made this idea popular.
Embeddings power meaning-based search and recommendations. RAG also uses them to find the passages closest to your question.
Think of it like this
Imagine a music app placing every song on a map: upbeat dance tracks cluster in one corner, slow ballads in another. Recommending songs near the ones you play is like using embeddings, which are coordinates on a map of meaning.
For example
A famous example: "king" minus "man" plus "woman" lands near "queen".
Records about this
Source-checked moments from AI history.
The starting point of every dense retrieval and embedding stack that followed
Proposed a neural language model that learns words as dense vectors. The direct predecessor of the embedding lineage that includes word2vec