Tokenization and Embeddings in Large Language Models (LLMs)

Large Language Models don’t understand text the way humans do—they work with numbers.

Tokenization

is the first step that bridges this gap. It breaks raw text into smaller units called tokens (words, subwords, or even characters). For example, “unbelievable” might be split into “un”, “believe”, and “able”. This approach helps LLMs handle rare words, spelling variations, and multiple languages efficiently while keeping the vocabulary size manageable. Once text is tokenized, each token is converted into a numerical representation called an embedding.

Embeddings

are dense vectors that capture the meaning of tokens based on their context. Tokens with similar meanings (like “king” and “queen”) end up closer together in this vector space. This semantic structure allows models to understand relationships, analogies, and context rather than just matching keywords.

Conclusion:

Together, tokenization and embeddings form the foundation of how LLMs read and reason about language. Tokenization structures the input, and embeddings give it meaning—enabling tasks like translation, question answering, search, and text generation. Understanding these concepts is key to grasping how modern AI language systems work under the hood.