Behind almost every "AI-powered search" and RAG feature sits the same quiet workhorse: similarity search over vectors. If you're building anything that finds relevant content by meaning rather than exact keywords, you need to understand vectors — and, importantly, when you do and don't need a dedicated database for them.
From text to vectors
An embedding is a list of numbers — a vector — that captures the meaning of a piece of text (or an image, or audio). An embedding model turns "cancel my subscription" and "how do I stop being billed" into vectors that sit close together, even though they share almost no words.
That closeness is the whole trick. Meaning becomes geometry: similar concepts land near each other in a high-dimensional space, and "find related content" becomes "find nearby vectors."
How similarity search works
