When I first started learning about Large Language Models, I was surprised to find out that they don’t actually read like we do. They don’t see words, sentences, or ideas. Instead, when a model like GPT-4 is trained on trillions of tokens, it’s not reading trillions of words. It’s working with something even more basic. So, what exactly are tokens in LLMs? Let’s take a closer look.
The ABC of Tokens in LLMs
At its simplest, a token is the fundamental unit of text for an LLM. Think of tokens as the LEGO bricks of language. You can’t give a model a whole, complex idea. You have to give it a set of standardized bricks (tokens) that it can understand and assemble.
The process of turning your prompt (“What is a token?”) into these bricks is called tokenization.
Here’s why this is the most important first step: computers don’t understand “A,” “p,” “p,” “l,” “e.” They understand numbers. Tokenization is the bridge. For example:
- Text: Hello, world!
- Tokenization: [“Hello”, “,”,””, “world”, “!”]
- Numerical IDs: [15496, 11, 220, 318, 0] (These numbers are just examples)
It’s these numbers, not the words, that get fed into the model’s massive neural network.
So, Why Tokens Matter to You
Okay, this is more than just a technical detail. Understanding tokens is essential for anyone studying or using generative AI. It’s the difference between being confused by a model and being in control of it.
Here’s where it hits the real world.
1. The Token Limit is Your Model’s Short-Term Memory
You’ve read this anywhere:
- “This model has a 4k context window.”
- or “The new model has a 200k token limit.”
This context window (or token limit) is the maximum number of tokens the model can see or remember at one time. This limit includes both your prompt (input) and the model’s answer (output).
Ever been in a long, detailed chat with an AI, and suddenly it seems to forget what you were talking about 20 messages ago? That’s because the start of your conversation just fell out of its context window. It’s not being dumb; its memory is literally full.
To have a long, coherent conversation, you must summarize past information and feed it back to the model, keeping your total token count under the limit.
2. Tokens = Money
When you use an LLM API (like from OpenAI or Google), you don’t pay per query. You pay per token.
This includes the tokens in your prompt and the tokens the model generates. A long, wordy prompt is literally more expensive than a short, concise one.
An inefficient process that asks the model to “think step by step” in a long-winded way will cost more than one that gets a short answer.
3. Not All Languages Are Tokenized Equal
A rule of thumb for English is that one token is about 4 characters, or ¾ of a word.
This rule completely breaks down for other languages.
Text in German, Turkish, or Korean often expands into many more tokens than the equivalent English text. This is because their tokenizers are often less optimized, or the language’s structure is just different.
This means it costs more, and you’ll hit your token limit faster when working with non-English languages. It’s a critical, practical bias to be aware of when building global applications.
Final Words
It’s tempting to dismiss tokens as a dry, technical implementation detail. I used to. But the more I work with these models, the more I see tokenization as something profound. It’s the point of contact between the messy, infinite, and beautiful complexity of human language and the cold, finite, and structured logic of a machine.
I hope you liked this article on what tokens are in LLMs. Feel free to ask valuable questions in the comments section below. You can follow me on Instagram for many more resources.
