What is Embedding?
It is the conversion of words or data into numerical strings so that they can be understood by artificial intelligence.
Overview
Embedding is a digital mapping method that allows computers to understand the relationship between words. Thanks to this process, words are converted into coordinates in a multidimensional space according to their semantic proximity.
How it works
By reading huge texts, the artificial intelligence model learns in what contexts words are used. Then, it assigns to each word a long series of numbers, that is, a vector, that represents its world of meaning. These numbers determine the semantic distance of the word from all other words.
Where it is used
It is used in search engines to bring results that are close in meaning even if they do not exactly match the word the user is looking for. It is also the fundamental building block in any application where artificial intelligence models need to make sense of text.
Commonly confused with
It is often thought of as just a translation method, but this is not a translation but a mathematical placement of meaning in space.
Frequently asked questions
Why don't we just use words?
Computers don't know what words mean, they can only process numbers.
Is embedding permanent?
Yes, numerical vectors created once are stored in the database and reused whenever needed.
Related terms
Related tools
This explanation was written in plain language for TreScout and machine-translated from the Turkish original · the Turkish version prevails. If something looks wrong or missing, write to hello@trescout.com. Read in Turkish →