Introduction

When we interact with an AI model, it feels like the system understands our words instantly, just like a human would. But that’s not what happens internally. AI models cannot read text directly. They cannot interpret letters, punctuation, or full sentences the way we do. Instead, they rely entirely on numbers. Tokenization is the crucial first step that transforms your text into a form the model can understand.

To simplify this idea, imagine a sentence as a completed LEGO house. If you want someone else to study how the house is built, you wouldn’t hand them the final model in one piece. You would dismantle it into individual LEGO bricks so they can see the structure and rebuild it themselves. Tokenization works exactly the same way. Instead of giving the AI your entire sentence as one unit, you break it down into small pieces called tokens. Each token becomes one LEGO brick the model can process.

Concept 1: What Are Tokens?

A token is a small unit of text. But what counts as “small” can vary. There are several levels of tokenization, and each model chooses one depending on what it was trained for.

Now you see how each approach has trade-offs. Modern language models typically use subword tokenization because it finds the right balance between flexibility, meaning, and efficiency.

Concept 2: Vocabulary — The AI’s Dictionary

Example vocabulary entries:

"play"        → 4721
"##ing"       → 3012
"AI"          → 8275
"."           → 1012

This list is created during training and never changes afterward. If a word doesn’t exist in the vocabulary, the tokenizer breaks it into known sub-parts.

Concept 3: Token IDs — What the Model Actually Sees

Concept 4: Sequence Length — How Much the Model Can “Read” at Once

Concept 5: Why Tokenization Matters

Tokenization affects almost every part of AI:

• Understanding: Smaller units help the model interpret meaning more accurately.
• Context: The number of tokens determines how much the model can “remember” at once.
• Speed: Fewer tokens → faster responses.
• Cost: Paid APIs charge per token.
• Accuracy: Clean tokens reduce ambiguity and errors.

Without tokenization, language models simply would not function.

Tokenization in Python (Practical Example)

Install the Transformers library:

pip install transformers

Tokenizing with BERT:

from transformers import BertTokenizer

tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")

sentence = "Tokenization is essential for understanding AI."

tokens = tokenizer.tokenize(sentence)
print("Tokens:", tokens)

token_ids = tokenizer.encode(sentence)
print("Token IDs:", token_ids)

Tokens: ['token', '##ization', 'is', 'essential', 'for', 'understanding', 'ai', '.']
Token IDs: [101, 19204, 3989, 2003, 6827, 2005, 4824, 9932, 1012, 102]

💡 Small Note on Token Count: Although the sentence breaks down into 8 content tokens, the final list of Token IDs has 10 numbers (101 at the start and 102 at the end). These extra IDs represent special tokens like [CLS] (Classification) and [SEP] (Separator) that the model needs to understand the sequence structure.

BERT splits the word “Tokenization” into two subwords; “token” and “##ization”, because BERT uses a WordPiece tokenizer.
In total, this sentence becomes 8 tokens, which BERT then converts into numeric token IDs that the model can process internally.

💡 Note: BPE (The GPT/LLaMA Method)

While our BERT example uses WordPiece (a style of subword tokenization), the majority of powerful modern LLMs (like GPT, GPT-4, and LLaMA) rely on Byte Pair Encoding (BPE).

BPE is a simple, iterative process that creates a smart vocabulary by repeatedly merging the most frequent adjacent pair of characters/subwords into a single new token. This balance ensures efficiency (common words are single tokens) and flexibility (rare words are broken down into known chunks).

For a more detailed comparison of BPE, WordPiece, and other methods, see the Hugging Face Summary of Tokenizers.

If you want to experiment with how other models tokenize the same sentence, you can use this interactive online tokenizer tool:

👉 https://tiktokenizer.vercel.app/

It lets you test how the text is tokenized across different models such as GPT-2, GPT-3.5, GPT-4o, LLaMA, Mistral, and more.
Just paste your sentence and select a model to instantly see:

• how many tokens it generates
• how each model splits words differently
• the numeric token IDs used internally

This is a great way to visually compare tokenization methods and understand why different models produce different token counts.

---

Frequently Asked Questions

Conclusion

Tokenization is the quiet but essential step that allows AI models to read and process text. It breaks language into smaller pieces, assigns them IDs, and prepares everything for deeper processing such as embeddings and attention. Once you understand tokens, vocabularies, token IDs, and sequence limits, you have the foundation needed to understand almost every topic in NLP.

If you want to explore more core AI concepts, visit the AI & ML Glossary for clear definitions of related terms.

In future posts, we’ll explore embeddings, vocabulary size, positional encoding, and how tokenization impacts cost and model performance.

If you’d like to get in touch, feel free to use our Contact Page.

For more articles, tutorials, and guides, check out our Blog Page.