What is an embedding, really? A plain-English guide
Embeddings sound like math you need a PhD for. The core idea is simpler and more useful than that, and it's the reason a cache can tell that two different sentences mean the same thing.
If you've heard the word 'embedding' and nodded along without quite knowing what it meant, this is for you. Strip away the jargon and it's a beautifully simple idea: turn a piece of text into a point in space, positioned so that things with similar meanings land near each other. 'Cancel my subscription' and 'stop my plan' end up as neighbours; 'what's the weather' lands far away.
An embedding places a sentence in space so nearby points mean nearby things.
This is the quiet engine under semantic search, recommendation, RAG, and semantic caching. When Crowkis decides that a new question matches an answer it already has, it's placing the question in this space and checking who its neighbours are. The model that draws this map, turning text into a point, is the embedding model. Ours is called crowsight, and it runs on your own machine.
The bottom line
An embedding is meaning with coordinates. Once you can measure the distance between two ideas, a cache can finally tell that 'reset my password' and 'forgot my login' are the same request.