WTF Are Tokens (Input and Output)?

If you've been playing around with ChatGPT, Claude, or any other AI model, you've probably seen references to "tokens" everywhere. Token limits, token pricing, input tokens, output tokens... But what the hell are tokens actually?

Let me break it down in simple terms.

What Are Tokens?

Think of tokens as the smallest units of text that an AI model can understand. It's like how words are made up of letters, but for AI, text is made up of tokens.

Here's the thing: tokens aren't always whole words. Sometimes they are, but sometimes they're parts of words, punctuation marks, or even spaces.

Examples of Tokenization

Let's see how the sentence "Hello, world!" might be tokenized:

"Hello, world!" → ["Hello", ",", " world", "!"]

Or this sentence: "I'm learning about tokenization"

"I'm learning about tokenization" → ["I", "'m", " learning", " about", " token", "ization"]

Notice how:

  • "Hello" is one token
  • The comma "," is its own token
  • "world" includes the space before it
  • "tokenization" got split into "token" and "ization"

Why Do Tokens Exist?

AI models don't understand text the way humans do. They need to convert text into numbers to process it mathematically. Tokenization is the first step in this process.

The Process:

  1. Text → Tokens → Numbers → AI Magic → Numbers → Tokens → Text

Input vs Output Tokens

When you interact with an AI model, you're dealing with two types of tokens:

Input Tokens

These are the tokens from your prompt that you send to the AI. This includes:

  • Your question or instruction
  • Any context you provide
  • Previous conversation history (in chat models)
  • System prompts (the instructions that tell the AI how to behave)

Output Tokens

These are the tokens the AI generates in response. Basically, everything the AI writes back to you.

Why Should You Care About Tokens?

1. Cost

Most AI APIs charge based on tokens. You pay for both input and output tokens, often at different rates:

  • Input tokens: Usually cheaper (e.g., $0.01 per 1K tokens)
  • Output tokens: Usually more expensive (e.g., $0.03 per 1K tokens)

2. Limits

AI models have token limits. For example:

  • GPT-3.5: 4,096 tokens total (input + output)
  • GPT-4: 8,192 tokens (or more in newer versions)
  • Claude: Up to 200,000 tokens

3. Performance

More tokens = slower response and higher cost. Understanding tokens helps you write more efficient prompts.

How Many Tokens is That?

As a rough rule of thumb:

  • 1 token ≈ 0.75 words (in English)
  • 1 word ≈ 1.3 tokens
  • 100 tokens ≈ 75 words

So a 1,000-word article would be roughly 1,300 tokens.

Practical Examples

Example 1: Simple Question

Input: "What is the capital of France?" (7 tokens)
Output: "The capital of France is Paris." (7 tokens)
Total: 14 tokens

Example 2: Longer Prompt

Input: "Write a Python function that calculates the factorial of a number" (12 tokens)
Output: [A complete Python function with explanation] (~150 tokens)
Total: ~162 tokens

Token Optimization Tips

For Input Tokens:

  • Be concise but clear in your prompts
  • Remove unnecessary context
  • Use abbreviations where appropriate

For Output Tokens:

  • Ask for specific lengths ("in 100 words or less")
  • Request bullet points instead of paragraphs
  • Be specific about the format you want

The Technical Side (Optional)

If you're curious about the technical details:

  1. Tokenization uses algorithms like Byte Pair Encoding (BPE)
  2. Each token gets mapped to a unique number (token ID)
  3. These numbers become vectors (lists of numbers)
  4. The AI processes these vectors mathematically
  5. The output vectors get converted back to token IDs
  6. Token IDs become tokens, tokens become text

Common Misconceptions

"One token = one word"

False. Tokens can be parts of words, whole words, or multiple characters.

"Spaces don't count as tokens"

False. Spaces are often included with the following word as part of the token.

"Token limits are suggestions"

False. Token limits are hard limits. Exceed them, and your request gets cut off.

Tools to Explore Tokens

Want to see tokenization in action?

  • OpenAI's Tokenizer tool
  • Hugging Face's tokenizer playground
  • Various online token calculators

Conclusion

Tokens are the fundamental building blocks of how AI models process text. Understanding them helps you:

  • Write better prompts
  • Manage costs effectively
  • Work within model limitations
  • Debug issues with AI responses

Think of tokens as the "pixels" of AI text processing. You don't always need to think about individual pixels when looking at a photo, but understanding them helps when you need to optimize or troubleshoot.

Now that you understand tokens, you'll start noticing them everywhere in the AI world. Welcome to the matrix! 🤖