The infrastructure behind semantic search and RAG.
5 min read · Skill IT Education
Traditional databases match exact values. Vector databases match by meaning — finding content that's conceptually similar even without shared keywords.
Text gets converted into embeddings — vectors of numbers representing meaning — and the database finds vectors that are mathematically close to a query vector.
Every RAG system depends on fast, accurate similarity search across potentially millions of document chunks — vector databases are the infrastructure that makes this practical at scale.
This article is the short version. The full program covers it hands-on, in labs, with a mentor reviewing your work.
See RAG Systems