Word Embeddings Explained: The Math Behind AI, LLMs, and Chatbots

The explainer shows how word embeddings turn word meaning into high-dimensional vectors.

It uses cosine similarity, Euclidean distance, and dot products to compare words in semantic space.

Vector arithmetic can expose relationships like related terms, opposites, and analogies.

These ideas power modern NLP, search, large language models, and chatbots by measuring meaning rather than matching exact words.