What are tokens in AI?
Tokens are the basic units that AI language models use to process text. A token can be a word, part of a word, or a punctuation mark. Understanding tokens is essential for managing AI costs, optimizing prompt length, and working within context window limits.
Token Essentials
How tokens work in AI
Every feature designed to help your team work smarter with AI.
Tokenization
Text is split into tokens by the model's tokenizer. In English, one token is roughly three-quarters of a word or about four characters.
Pricing model
AI providers charge per token for both input (your prompt) and output (the response). Understanding tokens helps control costs.
Context window
The context window is measured in tokens. Both your prompt and the model's response consume tokens from the available window.
Speed impact
Longer prompts with more tokens take longer to process. Token-efficient prompts get faster responses.
Language differences
Tokenization varies by language. Non-English text often requires more tokens per word, affecting costs and context limits.
Token counting
Use tokenizer tools to count tokens in your prompts before sending them, ensuring they fit within limits and budget.
Benefits
Why understanding tokens matters
FAQ
Frequently asked questions
How many tokens is a typical prompt?
Simple prompts are 50-200 tokens. Detailed prompts with context are 500-2000 tokens. Complex prompts with examples and instructions can exceed 5000 tokens. TeamPrompt templates help standardize prompt length.
How does TeamPrompt help with token efficiency?
TeamPrompt's shared templates are optimized for clarity and efficiency. Instead of verbose one-off prompts, team members use concise, proven templates that achieve better results with fewer tokens.
Do input and output tokens cost the same?
No. Most AI providers charge differently for input and output tokens. Output tokens are typically more expensive. Check your provider's pricing to optimize costs.
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