How AI Uses Embeddings to Understand Meaning, Not Just Words

Embeddings are numerical representations of text, images, or audio that allow AI systems to capture and compare meaning mathematically. Unlike traditional keyword-based search, embedding models place semantically similar concepts close together in a multi-dimensional vector space, even when the exact words differ. For example, phrases like 'buy a flight' and 'book an airplane ticket' would be represented as nearby vectors despite sharing little vocabulary. Similarity between embeddings is measured using techniques such as cosine similarity, Euclidean distance, and nearest-neighbor search. This technology underpins modern applications like semantic search and Retrieval-Augmented Generation, where relevant content is retrieved based on meaning rather than literal text matches.