How AI Converts Words Into Numbers to Understand Meaning

Embeddings are lists of numbers that represent words, tokens, or other discrete inputs in a high-dimensional coordinate space, allowing machines to measure similarity between concepts. When a chatbot receives a query, it converts each token into a vector by looking up a large embedding matrix, turning raw token IDs into dense numerical representations. The model never truly learns the meaning of a word; instead, it learns which words appear in similar contexts, and that statistical pattern captures relationships like synonyms and analogies. These vectors serve as the foundation for downstream processes such as attention mechanisms, enabling the model to recognise that phrases like 'quick dinner idea' and 'fast evening meal suggestions' carry the same intent. Embeddings are not limited to language — recommendation systems and search engines use the same technique to represent users, products, and documents in comparable vector spaces.