What is Embedding?
A numerical representation of text (or images, audio, code) as a dense vector in high-dimensional space, where semantically similar content is positioned closer together. Embeddings capture meaning, not just keywords: "happy" and "joyful" land near each other, while "bank" (financial) and "bank" (river) land far apart based on context.
My perspective
In practice
Embeddings are the engine behind RAG retrieval, semantic search, recommendation systems, and clustering. When a user asks a question, it gets embedded into the same vector space as your knowledge base, and the closest matches are retrieved as context for the model. The quality of your embeddings (which model you use, how you chunk your documents, what metadata you include) directly determines the quality of your RAG system. Understanding embeddings is essential for anyone building or evaluating AI-powered knowledge systems.