Embeddings: How a Computer Gets Meaning
How words and ideas become numbers a machine can actually compare.
A computer can match exact words, but it has no idea that "car" and "automobile" mean the same thing; to it they are just different letters. An embedding is how a machine gets past that and starts comparing meaning.
What an embedding is
An embedding turns a piece of text into a list of numbers, a single point in space, placed so that things close in meaning land close together. "car" and "automobile" end up as near neighbors; "car" and "banana" end up far apart. The numbers themselves are not readable, but the distances between them carry the meaning.
A picture
Imagine a giant map where every word and sentence has a location, and similar ideas sit in the same neighborhood. That map is what an embedding model builds. To find related things, you do not search for matching words; you look for nearby points. In practice that map lives in a vector store.
Why it matters
This is the quiet engine under a lot of AI. Search by meaning instead of keywords, "find documents like this one," recommendations, and the retrieval step in RAG all work the same way: embed the question, then look for the nearest points. Once meaning becomes distance, "what is related to this?" turns into a simple math problem.