Tokens: The Tiny Building Blocks of AI !!

AI is everywhere !!

Since everybody is doing AI around me, I thought, let’s do it.

As soon as I started, I kept encountering new terms all the time. This time, the term that got stuck in my mind and made me do some research is “token.”

What is a Token?

At first glance, the word token feels small and insignificant. But in AI — especially in large language models (LLMs) like Gemini, Gemma, and GPT — a token is one of the most fundamental building blocks. 🙀

Simply put, a token is a chunk of text. But that chunk can vary: it might be as short as a single character or as long as a whole word. For example, the word “ChatGPT” could be one token, or it could be split into smaller tokens like “Chat” and “GPT,” depending on the model’s tokenizer.

Why Do We Break Text Into Tokens?

AI models don’t understand language the way humans do. They need to convert words into numbers — something they can compute. Tokens are the units that get converted into numbers (embeddings) and fed into the model.

If you think of AI as a massive recipe, tokens are the ingredients. The model doesn’t see whole sentences; it sees sequences of tokens and uses math to predict what token should come next.

How Are Tokens Formed?

Different models use different tokenizers, and what counts as a token can vary. In general:

• Common or short words may be a single token.
• Longer or more complex words might be broken into multiple tokens.
• Even punctuation or whitespace can be considered tokens.

Why Does This Matter to You?

• Pricing in AI APIs (like OpenAI’s GPT models) is often based on the number of tokens processed.
• The larger your prompt (in terms of tokens), the more expensive and potentially slower your request.
• Understanding tokens helps you craft more efficient prompts and predict how much input and output you can expect.

What Does “Tokens Remaining” Mean?

When you input text into an AI model, you might see a message saying something like “X number of tokens remaining.” This refers to the model’s maximum token limit, which includes both your input and the model’s output.

For example, if a model has a limit of 4,000 tokens, and your prompt is 500 tokens long, then the model has 3,500 tokens remaining for generating its response. This limit is crucial to keep in mind because:

• If your prompt is too long, it leaves less room for the model’s reply.
• If your conversation is ongoing, the history (chat context) also consumes tokens.
• When tokens run out, the model will stop responding, which can lead to incomplete answers.

In short, the tokens remaining are the model’s way of telling you how much space is left to continue the conversation or complete the output.

How to Increase Token Limits

Now, let’s talk about how you can increase the number of tokens your AI model can handle.

1. Choose a Model with a Larger Token Limit
   Some models are designed with a higher token capacity. For example, GPT-4 can handle up to 32,000 tokens, which is much more than earlier models like GPT-3 (4,096 tokens). By choosing models with a higher context window, you can process longer inputs and generate more extensive outputs.
2. Use Model Versions with Higher Token Capacity
   If you’re working with platforms like OpenAI, certain versions of models (like GPT-4) offer higher token limits, which allow you to work with more data in a single request.
3. Use Larger Context Windows (if Available)
   Some API versions or platforms let you set larger context windows, allowing you to push the token limits even further. This means you can provide the AI with more information for processing.
4. Chunk Your Data
   If your input text exceeds the model’s token limit, you can break it into smaller chunks. While this is a workaround, it allows you to process large datasets or long conversations by feeding them to the AI sequentially. Just make sure to manage how context is passed from one chunk to the next.
5. Optimize Your Prompts
   To make the most of your token limit, you can optimize your prompts. Use clear, concise language to avoid wasting tokens on unnecessary words. You can also use placeholders to dynamically insert text, saving tokens for the actual content.
6. Use Advanced Token Management Tools
   Some platforms provide tools to help you manage token usage. These tools allow you to track how many tokens you’re using in your input and output, giving you more control over your interactions with the AI.
7. Fine-Tuning Models
   If you’re developing your own AI models, you can train them to handle more tokens by adjusting the tokenizer or modifying the model architecture. This, however, requires significant technical expertise and resources.

A Fun Tip:

Next time you ask Gemini or ChatGPT a question, remember: behind the scenes, it’s working with thousands of tiny tokens — each one carefully predicted, one after another.

If you’re curious, try pasting your text into OpenAI’s Tokenizer Tool to see how your words are split!

In Short:

A token may seem small, but understanding it is a big step in understanding how AI communicates with us.