Embedding
A numeric vector representation of data that captures semantic meaning.
An embedding is a fixed-length numeric vector that a model learns to represent text, images, or other data so that semantically related items cluster close together in vector space. On AWS, Amazon Bedrock provides embedding models (such as Amazon Titan Embeddings) that convert input into these vectors, which are then stored in a vector database to power semantic search and retrieval-augmented generation (RAG) pipelines.
The key exam distinction is between an embedding model and a generative model: embedding models produce vectors for comparison and retrieval, not human-readable text. Confusing the two roles is a common trap — a generative model answers questions, while the embedding model finds the relevant context to feed it.
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