How Text Embeddings Power Semantic Search and Modern AI Applications

Embeddings are numerical representations of text — dense vectors of floating-point numbers — that capture the semantic meaning of language rather than its literal wording. Unlike traditional keyword search, embedding models map text into a high-dimensional vector space where phrases with similar meanings are placed close together, even if they share no common words. Similarity between vectors is measured using cosine similarity, which calculates the angle between two vectors to determine how semantically related they are. This technology is central to Retrieval-Augmented Generation (RAG) pipelines, where user queries and documents are both converted into vectors and compared via similarity search before being passed to a large language model. Embeddings underpin a wide range of AI features, including document retrieval and recommendation systems, making them a foundational concept for anyone building LLM-based applications.