Embeddings are underrated

Good explainer on what embeddings really are.

Here’s the mental leap. Embeddings are similar to points on a map. Each number in the embedding array is a dimension, similar to the X-Coordinates and Y-Coordinates from earlier. When an embedding model sends you back an array of 1000 numbers, it’s telling you the point where that text semantically lives in its 1000-dimension space, relative to all other texts. When we compare the distance between two embeddings in this 1000-dimension space, what we’re really doing is figuring out how semantically close or far apart those two texts are from each other.

If you are building custom LLM applications this is likely something you need to use.