Embeddings are vector representations of text, images, or other data in high-dimensional space. The key insight is that semantically similar items cluster together in embedding space. If you embed the word 'dog' and the word 'puppy,' they'll be close. If you embed 'dog' and 'car,' they'll be far apart. This geometric property makes embeddings powerful.
You can compute similarity by measuring distance. You can find nearest neighbors. You can cluster items by semantic meaning. LLMs generate embeddings for text. Each word, phrase, or entire document gets mapped to a vector in high-dimensional space. Documents about similar topics end up near each other. Documents about different topics spread apart.
Semantic search works by embedding queries and finding the nearest documents. Instead of keyword matching, you're matching meaning. The results are more accurate because you're finding documents semantically similar to your query, not just string matches. Embeddings power retrieval-augmented generation by embedding documents and user queries, then finding the closest matches.
They power recommendation systems by embedding products and users, finding products similar to past purchases. Embeddings are foundational to modern AI applications. They're the bridge between discrete language and continuous mathematics.