Understand how AI models process text and why tokens matter for costs.
Everything you need to know about tokens, prompts, and TOON for AI development.
A token is the basic unit of text that AI language models process. Think of it as the "atoms" of language for AI - the smallest pieces that the model works with. Unlike words, which humans naturally understand, tokens are optimized for how neural networks process information.
The term "token" comes from computer science and linguistics, where it refers to a single unit of meaning. Just like arcade tokens represent a unit of play, AI tokens represent a unit of text processing - and you pay for what you use.
The term originated in lexical analysis (compilers break code into tokens), natural language processing (text is tokenized for analysis), and information theory (discrete units for encoding information).
Modern AI models use Byte Pair Encoding (BPE) or similar algorithms. The process starts with individual characters, finds the most common pairs, merges them into new tokens, and repeats until the vocabulary size is reached.
Example tokenization:
"unhappiness" → ["un", "happiness"] or ["un", "happ", "iness"]
Why not just use words? Words vary wildly in length, many languages don't have clear word boundaries, new words need handling, and tokens provide consistent granularity.
| Text | Characters | Words | Tokens (approx) |
|---|---|---|---|
| "Hello, world!" | 13 | 2 | 4 |
| "Tokenization" | 12 | 1 | 3-4 |
| "AI is amazing!" | 14 | 3 | 4-5 |
Every AI model has a maximum token limit. GPT-4 Turbo supports 128K tokens, Claude 3 up to 200K.
API costs are calculated per token. Input tokens (your prompt) and output tokens are priced separately.
More context tokens = better understanding = better responses from the AI model.
"I would like you to please help me write a very detailed blog post about AI"
"Write a detailed blog post about AI"
Don't repeat instructions or context unnecessarily. Set instructions once in system prompts.
Use bullet points and clear formatting. Well-structured prompts often use fewer tokens.
A prompt is the input text you provide to an AI model to get a response. It's essentially your instruction or question that tells the AI what you want it to do. The quality of your prompt directly affects the quality of the AI's output.
Well-crafted prompts can dramatically improve AI responses. A vague prompt like "write about dogs" will get generic results, while "write a 200-word blog intro about golden retrievers as family pets, targeting first-time dog owners" gives the AI clear direction.
"Help me with my code"
"Debug this Python function that calculates factorial. It returns None for input 5"
Include relevant background information. If you're asking about code, include the programming language, framework, and error messages. For writing tasks, specify audience, tone, and purpose.
Start with "You are a [role]" to set expectations. Examples: "You are a senior Python developer", "You are a marketing copywriter specializing in B2B SaaS".
Tell the AI exactly how you want the response: "Return as JSON", "Use bullet points", "Write in markdown format", "Keep it under 100 words".
Show the AI examples of what you want. Provide 2-3 input/output pairs before your actual request to help the model understand the pattern you're looking for.
TOON (Token Optimized Object Notation) is a compact format for JSON data that minimizes token usage when sending data to AI APIs. By removing whitespace and abbreviating common keys, TOON can reduce your API costs by 30-50%.
{
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}{"ms":[{"r":"u","c":"Hello"}]}TOON works by applying consistent key abbreviations (like "messages" → "ms", "content" → "c", "role" → "r") and removing all unnecessary whitespace. The format is fully reversible - you can convert TOON back to standard JSON at any time.
For high-volume applications making thousands of API calls, TOON can significantly reduce monthly costs. A 40% reduction on a $1000/month bill saves $400.
Fewer tokens mean faster processing. AI models process tokens sequentially, so reducing input size can noticeably improve response times.
By reducing tokens used on structure, you have more room for actual content within the model's context window (e.g., GPT-4's 128K limit).
Chatbots with long conversation history, batch processing systems, real-time applications, and any high-frequency API integration.