What Is an Embedding?
An embedding is a numerical representation of a piece of content — a word, sentence, image, or document — expressed as a list of numbers (a vector) that captures something about its meaning. Content with similar meaning ends up with mathematically similar embeddings, which is what makes fast, meaning-based comparison possible.
Why Embeddings Matter
Embeddings are the foundation of semantic search (finding content by meaning rather than exact keyword match) and are the core mechanism behind RAG systems, which need to quickly find the most relevant document chunks for a given query. Without embeddings, that kind of meaning-based retrieval at scale wouldn't be practical.
How They're Created
An embedding model (a specific type of trained AI model) converts input content into its numerical vector representation. Different embedding models produce different vector spaces, so embeddings from one model generally aren't directly comparable to embeddings from a different model.
Where They're Stored and Used
Embeddings are typically stored in a vector database, which is built to quickly find embeddings that are mathematically closest to a given query embedding — the core operation behind semantic search and RAG retrieval.
Related Pages
Frequently Asked
Are embeddings the same as a model's parameters?
No — parameters are the internal values a model learns during training; an embedding is a specific numerical output representing a piece of content, produced by running that content through an embedding model.
Can I compare embeddings from two different models?
Generally no — different embedding models produce different, incompatible vector spaces, so embeddings should be compared only within the same model's output.
Do I need to understand embeddings to use AI tools?
No — as an end user, embeddings work invisibly behind the scenes in features like semantic search; understanding them matters more if you're building an AI-powered search or retrieval system yourself.
How do embeddings relate to RAG?
RAG's retrieval step relies on embeddings and a vector database to quickly find the most relevant content for a given query before passing it to the model.