Chad Callihan demonstrates a vector comparison:
Vectors consist of an ordered list of numbers that represent characteristics or meaning captured from data. That data could be words, images, or other files. An AI embedding model is used to analyze the data and translate it into a long list of numbers that becomes that data’s vector embedding. The closer the vector embeddings are to each other, the more closely related the meanings of the data are together.
While vectors are more commonly hundreds or thousands of numbers, we’re going to simplify it and make up three numbers for our example.
Click through for that comparison.