Embeddings

Embeddings are numerical representations of text (or images, or any data) that capture meaning. When you convert a sentence into an embedding, you get a list of numbers (called a vector) that represents what that sentence means. Similar sentences get similar numbers, which is how AI can find related content, answer questions from documents, and make recommendations. Think of it like coordinates on a map. Every piece of text gets a location in a high-dimensional space. Texts about similar topics end up near each other. A sentence about 'best restaurants in Paris' would be close to 'where to eat in France' and far from 'how to fix a bicycle.' Embeddings power most useful AI applications: semantic search (finding documents by meaning, not just keywords), RAG systems (retrieving relevant context for AI chatbots), recommendation engines, and document clustering. If you've used Perplexity's search, a company's AI help desk, or any 'smart' search feature, embeddings were working behind the scenes. Creating embeddings requires a model (OpenAI's text-embedding-3 is the most popular) and storing them requires a vector database (Pinecone, Weaviate, or Chroma). But for most business users, embeddings are invisible infrastructure. You interact with the applications they power, not the embeddings themselves.