Embeddings turn text into geometry. Two pieces of text with similar meaning land near each other in vector space, regardless of whether they share words. This is what makes semantic search possible: 'how do I cancel my subscription' and 'where do I unsubscribe' produce nearby vectors. Embedding models are themselves LLMs, just smaller and tuned for the embedding task.
Engineering notes from the Digitorn team. No marketing, no launch announcements, no "10 prompts that will change your life". Just the things we write that we'd want to read.