Short answer
An embedding maps an item to a list of numbers capturing meaning or features. Similar items have similar vectors, enabling semantic search and many ML pipelines.
Why it matters
Embeddings power recommendation, retrieval-augmented generation, deduplication, and clustering across modern AI stacks.
Diagram
Article
From Word2Vec to modern encoders
Static word vectors gave way to contextual embeddings from transformers. Sentence and document embeddings power semantic search and clustering.
Examples
FAQ retrieval
Embed customer questions and help articles; retrieve top matches before an LLM drafts an answer.
FAQ
Do embeddings understand meaning like humans?
They capture statistical similarity from training data—not human understanding or guaranteed truth.
Sources
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Efficient Estimation of Word Representations in Vector Space
arXivWord2Vec embeddings paper.
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
NAACL / arXivBERT pretraining paper.
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Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
NeurIPS / arXivOriginal RAG paper (Lewis et al.).