AI & LLMs
Embedding
A numeric vector that represents what a piece of text means, placing similar meanings near each other.
Reviewed by the RadarTrek editorial team · June 2026
An embedding maps a piece of text to a list of numbers (a vector) such that texts with similar meaning end up at nearby coordinates in that vector space. "Cancel my subscription" and "stop my plan" would land close together even though they share no exact words. Semantic search is just finding the nearest vectors to your query's embedding.
Why it matters
- —Embeddings are what make semantic search possible — matching by meaning, not just keyword overlap.
- —Choosing an embedding model is a real trade-off between dimensionality, cost, and multilingual support.
- —Embeddings are the first step of every RAG pipeline — both for indexing documents and for the user's query.
Where to learn this
🎓
Vector Embeddings — What They Are and How to Choose a Model
RAG and Vector Search course
This is the exact lesson that covers this term in depth — with examples, diagrams, and a hands-on exercise.