TokenLab: Interactive Understanding of Tokenization and Prediction Mechanisms in Large Language Models

TokenLab is a Hebrew RTL (Right-to-Left) educational website project that helps learners understand the complete process of how Large Language Models (LLMs) split text into tokens, assign token IDs, and predict the next token through interactive visualization. Maintained by idocarmi1, the project was released on GitHub on June 15, 2026 (project name: tokenlab-ai-course, link: https://github.com/idocarmi1/tokenlab-ai-course). Its core goal is to lower the learning threshold for LLM technology and enable more people to intuitively understand its internal working mechanisms.

Large Language Models (LLMs) such as ChatGPT, Claude, and Llama exhibit powerful language capabilities, but their internal mechanisms remain a 'black box' for most learners and developers. Tokenization is the first step in LLM text processing and a key link to understanding the model's 'language comprehension'. The TokenLab project aims to address this educational pain point by allowing learners to intuitively observe the processes of text splitting, token ID assignment, and prediction through an interactive Hebrew RTL website.

1. Text Tokenization Visualization

Real-time display of the process of splitting Hebrew text into tokens, helping to understand the reasons for word splitting, differences in token counts across languages, and handling methods for spaces and punctuation.

2. Token ID Mapping Display

Show the unique numeric code corresponding to each token, bidirectional mapping relationships, and the composition of vocabulary size.

3. Next Token Prediction Demo

Simulate the autoregressive generation capability of LLMs. Users can input text to observe candidate tokens and probability distributions, and explore the impact of temperature parameters on results.

4. RTL Language Support

Optimized for Hebrew and other RTL languages, including bidirectional text rendering, tokenization rules in RTL contexts, and handling of multi-language mixed input.

• Beginners: Quickly establish an intuitive understanding of tokenization through visualization, avoiding being intimidated by complex formulas.
• Developers: Understand the behavioral differences between different tokenizers (BPE, WordPiece, SentencePiece) to assist in model selection and optimization.
• Educators: Serve as a classroom auxiliary tool to help students deepen their theoretical understanding through experiments.
• Multilingual NLP Researchers: RTL support becomes a practical platform for studying the behavior of non-English LLMs.

• Frontend: Use modern web frameworks (such as React/Vue) to build interactive UIs, focusing on RTL text rendering and animations.
• Tokenization Engine: Integrate existing tokenization libraries or call the OpenAI Tokenizer API to achieve real-time splitting and ID query.
• Prediction Demo: Use lightweight models or precomputed probability distributions to simulate predictions, ensuring smooth browser operation.
• Internationalization: Consider details such as Unicode processing and font rendering to support Hebrew and other RTL languages.

Limitations: Demonstration nature—shows a simplified conceptual model rather than a complete implementation of a production-level LLM (e.g., cannot perform real-time calculations of billions of parameters in the browser). Outlook: Expand support for more languages (such as Chinese and other CJK languages), compare differences between different tokenization algorithms, add attention mechanism visualization, and provide complete course materials and exercises.

TokenLab represents an innovative direction in technical education—lowering the cognitive threshold of complex AI concepts through interactive visualization. Today, with the popularity of LLMs, helping more people understand their working principles not only cultivates technical talents but also helps ordinary users use tools rationally. For the Hebrew community, RTL support fills the gap in multilingual AI education resources.