What are word embeddings? From Word2Vec to modern embedding models

Word embeddings convert language into dense numerical vectors that place terms used in similar contexts near each other, overcoming the sparsity and lack of semantic relationships in one-hot and count-based representations. Classic static approaches such as Word2Vec, GloVe, and fastText learned one vector per word, while ELMo and transformer models introduced contextual representations that vary with surrounding text. Current practice focuses primarily on text or sentence embeddings, which represent passages as single vectors for retrieval, classification, clustering, and RAG systems, often using contrastive training and task-specific query or document modes. Modern hosted and open-weight models offer flexible dimensions, long context windows, multimodal capabilities, and quantized outputs, making storage, latency, and retrieval quality important engineering tradeoffs. Effective RAG depends not only on the embedding model but also on chunking, hybrid keyword and vector search, reranking, and accurate source material. The same vector principles also support speaker identification and semantic search over transcribed audio, where transcript errors can directly reduce retrieval reliability.

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