How Large Language Models (LLMs) Understand Text: Intro to Tokenization

Large language models (LLMs) are the foundation of modern AI models, including generative and agentic AI. Gartner expects that, by 2026, over 30% of the increase in demand for APIs will come from AI and tools using LLMs—a testament to the technology’s rising prevalence.

Despite wide AI adoption, few users actually understand how these systems process text. This lack of information leaves users in the dark concerning actual text generation: Is it plagiarism, true thinking, or simple magic? 

The not-so-secret secret behind AI text processing is surprisingly simple: tokens. 

Every input and output an LLM handles consists of these building blocks, which play a much bigger role than most people realize. A working knowledge of what text tokenization in AI is, as well as how it works, is key to understanding how language models produce text.

Want to learn more about building scalable LLMs? Read How to Choose Your AI Backbone. 

What is a Text Token?

Text tokens are the bite-sized pieces LLMs break language into in order to process them as raw data. In slightly more technical teams, Microsoft defines tokens as the “words, character sets, or combinations of words and punctuation that are generated by large language models (LLMs) when they decompose text.” 

Tokens are words, subwords, and characters converted into a format easier for the model to understand. For example, an LLM using word-based tokens might take the sentence “Sally sat in a chair” and break it up into:

• Sally (1)
• sat (2)
• in (3)
• a (4)
• chair (5)

In this case, each word would be a token, with each having its own unique ID. These tokens exist in the LLM’s vocabulary, which can feature thousands of individual tokens.

Why is Tokenization Important for LLMs?

Given the number of steps involved, it’s natural to wonder why LLMs use tokens instead of directly processing full words and sentences. The simple answer is that natural human language is complicated and full of different variables. 

LLMs are large data processing models: They don’t think, remember, or even process information in the way we do. Rather, they examine data patterns and assemble the most logical output based on massive amounts of data.

To do this, the LLM must process billions of phrases By breaking the text down into smaller and more predictable units, the LLM can analyze, process, and detect patterns in data much more efficiently. 

Interested in building an LLM-powered system for your business? Explore how FullStack builds custom AI solutions.

How Does LLM Tokenization Work?

We understand what tokens are, but how do LLMs tokenize text?  Once the model receives its training data, the tokenization process begins with a few essential steps:

1. Normalize the Text

The first step to tokenizing text is standardization. In this step, the raw text is standardized: lowercased, cleaned of excess punctuation or spacing, and stripped of inconsistencies. This creates a more uniform base for the tokenizer to work with.

2. Split into Tokens

The LLM then splits the text into tokens. A tokenization algorithm (like Byte Pair Encoding or WordPiece) breaks the text into smaller units based on patterns found in the training data.

These might be whole words, subwords, or characters, depending on the model’s design. If you’re curious about what this tokenization process looks like, OpenAI offers a tool demonstrating how an LLM might tokenize text and how many tokens that text would be. 

3. Assign Token IDs

Each token is matched with a unique numerical ID from the model’s vocabulary. This step effectively converts LLM text to numbers, which then becomes the actual input the model uses during training and inference. As the LLM continues to learn and train, each new word, character, or subword is assigned a new ID, letting it build a vast vocabulary over time. 

4. Package into Sequences

Once all the text has been turned into token IDs, the AI then groups them into smaller chunks called sequences. The model then studies one sequence at a time and learns how each token relates to the ones before it. By repeatedly doing this, it analyzes the patterns in language, then uses that information to guess what should come next when it generates text later on. 

Tokenization Examples in AI

Different systems use different types of LLM tokenization.

Let's examine three LLM tokenization methods, and how they might tokenize the same phrase, "unbelievable performance."

Character Tokenization

Each token consists of individual characters. While this allows the model to handle a wider range of inputs, it creates more tokens that require additional computational resources.

Example

["u", "n", "b", "e", "l", "i", "e", "v", "a", "b", "l", "e", " ", "p", "e", "r", "f", "o", "r", "m", "a", "n", "c", "e"]

Total tokens: 24

Pros: Handles any string, even misspellings or unknown words

Cons: Increases token count, reducing efficiency

Word Tokenization

The word tokenization method, as demonstrated earlier, converts each word into a token, creating a smaller number of large tokens. This allows the LLM to create larger inputs, but may require more memory resources.

Example

["unbelievable", "performance"]

Total tokens: 2

Pros: Fewer tokens, better for shorter sequences

Cons: Struggles with rare, new, or misspelled words

Subword tokenization

When LLMs use subword tokenization, the system divides each word into meaningful chunks, rather than whole words. It acts as a middle ground between word and character tokenization, producing fewer tokens while still allowing the model to better handle rare or unfamiliar words.

Example
A likely subword tokenization (e.g., with GPT-2 or SentencePiece) might look like:

["un", "believ", "able", "per", "formance"]

Or in another scheme:

["un", "believable", "per", "formance"]

Or, with BPE (Byte Pair Encoding) specifically:

["un", "bel", "iev", "able", "per", "form", "ance"]

Pros: Balances efficiency and vocabulary flexibility

Cons: Slightly more tokens than word-level, still needs careful vocabulary tuning

Why Does Knowing How LLMs Tokenize Text Matter? 

While the tokenization process happens behind the scenes, it plays a key part in what LLMs do and how they operate. By understanding how LLMs tokenize text, companies gain clearer insight into how their AI system interprets input, generates output, and makes decisions, improving explainability and lessening the risks of black boxes. 

Additionally, when a company understands how its LLMs work, it can more effectively address common misconceptions around them, helping both users and employees feel more confident and comfortable using AI.

If you’re interested in learning more about the importance of explainability, you can read more about data privacy implications in AI adoption. And for practical inspiration, learn how companies are applying generative AI to gain a competitive edge.

‍Ready to find your AI development partner? Contact Us Today!