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Embeddings convert text into numerical vectors that encode meaning, placing semantically similar content near each other in a high-dimensional space. This solves the vocabulary mismatch problem where keyword search fails to connect 'charged twice' with 'duplicate transaction.' The post explains how embedding models work via contrastive training, walks through a 6-step RAG retrieval pipeline with code, and covers real-world implementations at Spotify, DoorDash, and Pinterest. It also addresses key pitfalls: the dimensionality cost tradeoff, embedding drift over time, cold start in niche domains, and a fundamental geometric ceiling proven by Google DeepMind showing single-vector embeddings have hard retrieval limits. The honest conclusion is that production systems combine embeddings with BM25 hybrid search and rerankers rather than relying on embeddings alone.