Technique Updated 2026-04

Embedding

Definition

An embedding is a numerical representation (vector) of text or data, capturing its semantic meaning.

See also in the glossary

V
Vector Database
A vector database stores embeddings for semantic search and RAG at scale.
R
RAG (Retrieval-Augmented Generation)
RAG is a technique that connects an LLM to external data sources to generate more accurate and up-to-date answers.
L
LLM (Large Language Model)
An LLM is an AI model trained on billions of texts, capable of understanding and generating human language.
N
NLP (Natural Language Processing)
NLP is the field of AI that enables machines to understand, interpret and generate human language.

Tools that use embedding

P
Pinecone
The leading vector database for AI applications
4.6/5
P
Perplexity
The AI answer engine that cites its sources
4.5/5
C
Consensus
The AI search engine for scientific literature
4.7/5
N
NotebookLM
Google's AI that turns your docs into an expert assistant
4.5/5

Frequently Asked Questions

What are embeddings used for?
They measure semantic similarity between texts. Two sentences with the same meaning will have close vectors. It's the foundation of RAG.
Are embeddings and tokens the same?
No. A token is raw text. An embedding is its translation into a numerical vector in mathematical space.