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Token Management

Understanding Tokens in AI Models​

Tokens are the basic units of text that language models use to process and generate language. Tokens can represent words, parts of words, or even single characters, depending on the language and the tokenizer used by the model. For example, the word "chatbot" could be a single token, or split into "chat" and "bot" as two tokens.

Types of Tokens​

  • Input Tokens: The tokens included in your prompt or message when sending a request to the AI model.
  • Output Tokens: The tokens generated by the model in its response.

Most AI APIs track both input and output tokens for each interaction.

Token Limits​

Each AI model has a maximum token limit, which is the sum of input and output tokens that a single request can handle.

For example:

  • If a model’s limit is 4096 tokens,
    • You could send a 2048-token prompt and get a 2048-token reply, or
    • Send a 3900-token prompt and request a 196-token reply.

What Happens at the Token Limit?​

  • If your prompt plus desired output exceeds the limit:
    • The model might truncate your prompt (from the beginning or end, depending on implementation).
    • Your request may fail or return an error.
    • The model will stop generating more tokens once the maximum output is reached, possibly cutting off mid-sentence.

Strategies for Managing Token Limits​

1. Monitor Token Usage​

  • Use token counting tools (many APIs provide token counting utilities).
  • Keep prompts concise.

2. Truncate or Summarize History​

  • For long conversations, keep only the most recent or most relevant part of the discussion.
  • Summarize previous messages to reduce total token usage.

3. Sliding Window Approach​

  • Maintain a window of the latest interactions that together fit within the token limit.

4. Systematic Reset​

  • If you hit the token ceiling:
    • Summarize the conversation and start a new session.
    • Store important context and pass it along in summarized form.

5. Optimize Output Length​

  • Specify a practical maximum for output tokens if your prompt is large.
  • Tune the max_tokens parameter (if available in your AI API).

Practical Tips​

  • Be concise: Longer conversations take up more tokens.
  • Chunk information: Break requests into smaller questions if needed.
  • Summarize as you go: Regularly condense the conversation.

Summary Table​

ScenarioAction
Exceeding token limit (error)Shorten prompt, summarize, or split conversation
Close to maximum input tokensOptimize by removing less relevant context
Need lengthy outputReduce input size or generate output in segments

By understanding and actively managing tokens, you can maintain effective and smooth conversations with AI models, even with strict token limits.