Vectors and RAG Systems from Nitin Borwankar's Perspective

The article analyzes the evolution of data storage: from traditional relational databases, serving as digital archives, to modern vector representations. The author explains the concept of embeddings as a process of translating human meaning into multi-dimensional geometry, allowing machines to achieve semantic understanding instead of simple keyword matching. The text emphasizes the difference between sparse and dense vectors and points out that vectors do not 'understand' in the human sense, but rather encode mathematical regularities and similarities. This transition from indexing to context mapping forms the foundation of RAG systems and modern AI infrastructure, enabling machines to operate on kinship of meaning rather than just character identity.