Tokenization Methods

Interviewers love to ask about byte-pair encoding (BPE) tokenization because it’s fundamental to how modern language models process text. You’ll often get this question at companies like OpenAI, Google, and other AI-driven firms since BPE is the core of popular models (OpenAI’s GPT series, for example)​. They want to see that you understand how text is broken down for a model, not just that you can call a library function. In other words, explaining BPE well shows you grasp the foundations of tokenization, how models handle vocabulary, and how to deal with new or rare words. It’s a chance for you to demonstrate practical knowledge of tokenization beyond buzzwords.

In this lesson, we’ll discuss different tokenization approaches, highlight why tokenization (especially subword methods like BPE) is beneficial, and dig into byte-pair encoding. We’ll even implement a simple BPE tokenizer in Python, step by step, to solidify your understanding.

What is tokenization?

In simple terms, tokenization means chopping a stream of text into pieces that are easier for a machine to handle. Each piece is a token—it could be a whole word, a part of a word, a single character, or even a punctuation mark. For example, tokenizing the sentence “GenAI is awesome!” might yield the tokens ["GenAI", "is", "awesome", and "!"]. These tokens are then mapped to numerical IDs to feed the text into a neural network​.

Why do we tokenize, though? Because these AI models operate on numbers. Tokenization bridges the gap between human language and machine-readable input by translating text into a sequence of token IDs. Instead of trying to assign a unique number to every possible string (an impossible task), we break text into consistent chunks from a fixed vocabulary. This makes processing computationally feasible and helps models learn patterns. Imagine reading a book one letter at a time vs. one word at a time—the latter is far more efficient. Likewise, tokenization finds the right-sized pieces for the model to process.

There are a few common strategies for tokenizing text, each with pros and cons, that you should always discuss in your interviews. The main types are word, character, and subword tokenization:

• Word tokenization: This splits text on word boundaries (often using spaces or punctuation as delimiters). Each token is a full word. For example, “tokens are great” → ["tokens", "are", and "great"]. Word tokenization is intuitive, but can lead to a huge vocabulary (every distinct word) and struggles with unknown words. If a model sees a word it has never encountered during training, it becomes an out-of-vocabulary (OOV) problem.

• Character tokenization: This breaks text into individual characters​. For instance, “hello” → ["h", "e", "l", "l", and "o"]. The advantage is a very small vocabulary (just the alphabet, etc.), which completely avoids unknown tokens – the model can spell out any word. However, sequences become very long, and each token (single character) carries minimal meaning, making it harder for the model to learn higher-level patterns​.

• Subword tokenization: This is a compromise between word and character tokenization​. The idea is to keep common words intact as single tokens, but break rarer or complex words into meaningful pieces (subwords). For example, the word “unbelievable” might be split into subword tokens ["un" and "believable"]—or even ["un", "believ", and "able"]—depending on what subword units are common in the training corpus. Subword methods ensure that some combination of known pieces can represent any new word. This balance moderates vocabulary size while retaining more meaning per token than pure characters.

Most state-of-the-art language models use subword tokenization because it hits the “sweet spot” between coarse and fine-grained​. It handles morphology (prefixes, suffixes, roots) elegantly—e.g., if the model knows “play” and “ing” separately, it can understand “playing,” and ...