AI Lesson 97 – Tokenization of LLMs | Dataplexa

AI Course

I. AI Foundations
1. Introduction to AI 2. History of AI 3. AI vs ML vs DL 4. Search Algorithms 5. Intelligent Agents 6. Informed & Uninformed Search 7. Constraint Satisfaction 8. Adversarial Search 9. Logic in AI 10. Planning in AI 11. Knowledge Representation 12. Probabilistic AI 13. Bayesian Networks 14. Markov Models 15. AI Ethics 16. AI Applications 17. Intro to PyTorch 18. Intro to TensorFlow 19. AI Workflows 20. Data for AI
II. Machine Learning
21. Introduction to ML 22. ML Workflow 23. Supervised Learning 24. Unsupervised Learning 25. Reinforcement Learning 26. Linear Regression 27. Logistic Regression 28. SVM 29. Decision Trees 30. Random Forest 31. Gradient Boosting 32. XGBoost 33. K-Means 34. Hierarchical Clustering 35. Dimensionality Reduction 36. Feature Engineering 37. Feature Selection 38. Model Evaluation 39. Overfitting vs Underfitting 40. Cross Validation
III. Deep Learning
41. Intro to Deep Learning 42. Neural Networks 43. Activation Functions 44. Backpropagation 45. Loss & Optimizers 46. Regularization 47. CNN Basics 48. CNN Architectures 49. RNN, LSTM, GRU 50. Seq2Seq 51. Transformers 52. Attention 53. Positional Encoding 54. Training DL 55. Hyperparameter Tuning 56. Transfer Learning 57. Autoencoders 58. GAN Basics 59. GAN Applications 60. Model Serving
IV. Natural Language Processing
61. Intro to NLP 62. Text Processing 63. Tokenization 64. Stopwords & Lemmatization 65. BoW & TF-IDF 66. Word Embeddings 67. Sentence Embeddings 68. NLP Classification 69. Sequence Modeling 70. RNN in NLP 71. Transformers in NLP 72. BERT Basics 73. BERT Fine-tuning 74. RoBERTa & DistilBERT 75. Sentiment Analysis 76. NER 77. Machine Translation 78. Text Generation 79. NLP Evaluation 80. NLP Use Cases
V. Computer Vision
81. Intro to CV 82. Image Processing 83. Filters & Edges 84. Convolutions 85. Object Detection 86. Object Tracking 87. Image Segmentation 88. Face Recognition 89. Feature Detection 90. OCR 91. Image Augmentation 92. Generative Vision 93. Vision Transformers 94. Multimodal Models 95. CV Use Cases
VI. LLM Engineering
96. Intro to LLMs 97. Tokenization 98. LLM Architecture 99. Pretraining 100. Fine-tuning 101. RLHF 102. Prompt Engineering 103. Embedding Models 104. Vector Databases 105. LLM Agents 106. Guardrails & Safety 107. AI in Production 108. End-to-End Project

Lesson 97: Tokenization in Large Language Models

Before a Large Language Model can understand or generate text, it must first convert human language into a format it can process. This conversion step is called tokenization, and it is one of the most critical parts of any LLM system.

In this lesson, you will learn what tokenization is, why it matters, how tokens are created, and how tokenization affects model performance and output.

What Is Tokenization?

Tokenization is the process of breaking text into smaller units called tokens. These tokens are the basic building blocks that an LLM works with.

A token can be:

  • A full word
  • A part of a word
  • A character or symbol

LLMs do not directly understand sentences or paragraphs. They only understand sequences of tokens.

Real-World Analogy

Think of tokenization like breaking a sentence into Lego blocks. You cannot build anything until the blocks are separated and ready to be assembled in different ways.

Similarly, LLMs cannot process raw text until it is broken into tokens.

Simple Tokenization Example

Consider the sentence:

"AI models are powerful"

A simple word-based tokenizer might convert it into:

  • AI
  • models
  • are
  • powerful

However, modern LLMs usually use more advanced tokenization methods.

Why Tokenization Is Important

Tokenization directly affects how well a model understands text.

  • It controls how text is represented internally
  • It impacts model accuracy and efficiency
  • It affects how much text fits into context limits

Poor tokenization can lead to misunderstandings, higher costs, and lower-quality outputs.

Subword Tokenization (Used by LLMs)

Most modern LLMs use subword tokenization. Instead of splitting text only into full words, they split words into smaller meaningful parts.

For example, the word:

"tokenization"

May be split as:

  • token
  • ization

This allows models to handle new or rare words without needing to learn them from scratch.

Tokenization in Code (Conceptual)


text = "Artificial intelligence is powerful"

tokens = tokenizer.encode(text)

print(tokens)

In this example, the tokenizer converts text into numerical token IDs that the model can process.

Token IDs and Vocabulary

Each token is mapped to a unique number called a token ID. The complete list of tokens is called the model’s vocabulary.

  • Text → Tokens → Token IDs
  • Model processes token IDs, not words
  • Output token IDs are converted back to text

Token Length and Context Window

LLMs have a maximum number of tokens they can handle at one time, called the context window.

Longer text means more tokens, which can:

  • Increase cost
  • Limit how much information fits
  • Cause older context to be forgotten

Efficient tokenization helps make better use of this limited space.

Common Tokenization Challenges

Tokenization is not always perfect.

  • Different languages tokenize differently
  • Special characters may become separate tokens
  • Small spelling changes can alter token count

Understanding tokenization helps you design better prompts and applications.

Practice Questions

Practice 1: What is the process of converting text into tokens called?



Practice 2: What are the basic units an LLM processes?



Practice 3: What type of tokenization is commonly used by modern LLMs?



Quick Quiz

Quiz 1: How does an LLM internally represent tokens?





Quiz 2: What limits how many tokens a model can process at once?





Quiz 3: Why is good tokenization important?





Coming up next: LLM Architecture — how transformers process tokens and build understanding.

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