AI engineering glossary · RAG vector search
What is vector normalisation?
Vector normalisation (also called L2 normalisation): Scaling a vector to have length 1 by dividing it by its norm. With normalised vectors, dot product equals cosine similarity, so systems can use the faster dot product for angle-based search.
Vector normalisation: an example
The vector [3, 4] has norm 5 and becomes [0.6, 0.8].
Related terms
More RAG vector search terms
- Vector quantisation
- Abstention
- Access control in retrieval
- Agentic RAG
- Alpha weighting
- ANN recall
- Answer correctness
- Answer relevance
Where you learn it
Vector normalisation is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.