Embeddings may be the least understood yet most valuable tool to come out of the world of Large Language Models. In this presentation, we will unravel the mystery of embeddings, emphasizing their mathematical foundations and practical applications.

We'll start by discussing what embeddings are, and what they represent. Then we'll explore the variety of tools we have to compare and contrast them, including Cosine similarity and distance, as well as clustering. Then we'll put those tools to use creating powerful applications that go beyond just typical chat and analytics use-cases.

Attendees will leave with a deeper understanding of the mathematical underpinnings of embeddings, practical knowledge of how to use them, and an appreciation for their value in our applications. This session is ideal for developers, data scientists, and anyone interested in the mathematical underpinnings of machine learning and natural language processing.