Tokens and Embeddings: The Building Blocks of GenAI

Generative AI (GenAI) is transforming how we interact with machines, enabling them to understand and generate human-like text. At the core of this revolution lie two fundamental concepts: tokens and embeddings. These elements form the foundation of how AI processes language, making them essential for anyone looking to understand or optimize AI models. Let’s explore them in detail.

Understanding Tokens

What are Tokens?

Tokens are the basic units of text that a language model processes. Instead of reading an entire paragraph or sentence at once, models break down the text into smaller parts called tokens. These tokens can be words, subwords, or even characters, depending on the tokenizer used.

Tokenization Process

Tokenization is the method of splitting text into manageable pieces. Different tokenization approaches include:

• Word-based Tokenization: Splits text by spaces, treating each word as a token (e.g., "Artificial Intelligence" → ["Artificial", "Intelligence"]).
• Subword-based Tokenization: Uses common word fragments to optimize token usage (e.g., "unhappiness" → ["un", "happiness"]).
• Character-based Tokenization: Treats each character as a separate token (e.g., "AI" → ["A", "I"]).

Steps in Tokenization

Tokenization involves following steps:

1. Normalization – Convert text to lowercase, remove punctuation and special symbols.
2. Splitting – Break the text into tokens (words, sub-words, or characters).
3. Mapping – Assign a unique number (ID) to each token.
4. Adding Special Tokens – AI models use extra tokens to help understand input structure.
   • CLS → Start of the sentence
   • SEP → Separates different parts of the text

Embeddings Explained

What are Embeddings?

Embeddings are numerical representations of words, phrases, or sentences in a multi-dimensional space. They help AI models understand semantic relationships between different pieces of text by converting them into vectors.

Vector Representations

Each word or token is mapped to a vector in an n-dimensional space. Words with similar meanings have vectors that are close to each other. For example:

• "King" and "Queen" will have similar embeddings.
• "Apple" (fruit) and "Apple" (company) may have different embeddings based on context.

How Do Embeddings Work?

• Each token is turned into a high-dimensional vector (a long list of numbers).
• AI learns relationships between words based on their meanings.
• The model uses these vectors to understand, generate, and predict text.

Where Are Embeddings Used?

• Chatbots & Virtual Assistants → Understand and respond to text.
• Search Engines → Find similar words and related topics.
• Recommendation Systems → Suggest videos, movies, or articles based on text.

Conclusion

Tokens and embeddings are the backbone of generative AI. Tokens help break down text into processable units, while embeddings provide the contextual and semantic depth necessary for AI to generate meaningful responses. Mastering these concepts allows developers to optimize AI models for better efficiency and accuracy, paving the way for more sophisticated and human-like interactions.

References

• Tokenization vs Embedding - How are they Different? by Airbyte
• Understanding the Core Components of LLMs: Vectors, Tokens, and Embeddings Explained by Vipin Jain