How Vector Databases Make AI Smarter
Betatalks #122
Christian and Yvo explain how vector databases improve search by focusing on meaning rather than exact words. Using embeddings, concepts are represented in a multi-dimensional space, revealing semantic links like “king - man + woman = queen.” Christian shows how this allows AI to understand intent and retrieve relevant results without exact matches. They conclude that combining vector search with language models makes AI more intuitive and context-aware.
Back to the overview of all Betatalks
