Step 7 of 15
What Are Embeddings? (Turning Words Into Numbers)
TL;DR: Embeddings turn words into lists of numbers that capture meaning. Things that mean similar things get similar numbers — so computers can search by meaning, not just exact words.
Explain it like I'm 5
Imagine giving every word a spot on a giant map. Words that mean similar things sit close together — "puppy" and "dog" are neighbors, but "dog" and "pizza" are far apart. Embeddings are those map coordinates. The computer uses distance to know what's related.
What is an embedding?
An embedding is a list of numbers that stands for the meaning of a piece of text. A special model reads your text and spits out this number list. Similar meanings produce similar lists.
Why turn words into numbers?
Because computers are great at math and bad at meaning. Once words are numbers, the computer can measure how close two ideas are just by comparing their numbers. That's how "search by meaning" works.
A simple example
Search the word "car" with plain text matching and you miss a page that only says "automobile." With embeddings, "car" and "automobile" have nearly the same numbers, so the computer finds it anyway — because it understood the meaning.
Where you'll use embeddings
Embeddings power smart search, recommendations, and — most importantly for AI engineers — feeding the right facts to an LLM. They're the first half of RAG, which you'll meet in two steps.
Frequently asked questions
Do I need to understand the math?
No. You just need to know that embeddings turn text into numbers that capture meaning, and that closer numbers mean more similar meaning.
What creates embeddings?
A special embedding model, separate from the chat model. You send it text and it returns the number list. Many providers offer one via an API.
Are embeddings only for text?
No. You can also make embeddings for images, audio, and more — the same idea of turning things into meaning-numbers applies.
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