A practical deep-dive into vector embeddings and cosine similarity — the mathematical foundation that makes retrieval in RAG systems actually work.

A practical guide for software engineers covering how vector databases use Approximate Nearest Neighbor algorithms to search millions of embeddings efficiently, and how to chunk documents intelligently so your RAG pipeline actually retrieves useful, precise context.