Large Language Models (LLMs) first require successful “non-large” language models. We’ll look at word embeddings, a technique for encoding words as vectors that capture their semantic meaning. We’ll examine the popular word2vec method and build/train a model to generate our own word embeddings. Finally we’ll explore how to perform linguistic operations using vector arithmetic with word2vec.