Embeddings, explained
Embeddings are numerical representations of text (or images, audio, etc.) that capture meaning — so that similar concepts end up close together in mathematical space, even if the words are different.
Computers work with numbers, not meaning. Embeddings are how AI bridges that gap. When you feed a sentence into an embedding model, it outputs a long list of numbers — typically hundreds or thousands of them — called a vector. The key property is that sentences with similar meanings produce similar vectors. 'The dog ran fast' and 'The hound sprinted quickly' will be much closer together numerically than 'The dog ran fast' and 'The quarterly report is due.'
This is what makes semantic search possible. A traditional keyword search finds pages that contain your exact words. A search built on embeddings finds content that matches your intent, even when different words are used. It's also the foundation of recommendation systems, duplicate detection, and clustering similar customer feedback.
Embeddings are usually generated by a separate, specialized model and then stored in a vector database for fast lookup. You don't have to build them yourself — services like OpenAI, Cohere, and Google expose embedding models through an API. Understanding what they are helps you make sense of why AI search feels fundamentally different from older keyword-matching approaches.
Go deeper
Wield's Data & Analysis track covers this hands-on, in plain English, with real examples and a copy-paste prompt to try it yourself.
Learn it, or have it done for you
Understanding the term is step one; using it well is the course. Start the course free and build a working AI habit yourself — or, if you'd rather skip to the outcome, MCF Agentic builds the AI workflows into your business directly.