Use Prompt Caching to Reduce Input Tokens with Claude

The article discusses how to use prompt caching to reduce input tokens when working with Anthropic's Claude model. Here’s a summary of key points:

Key Concepts:

1. Prompt Caching: A technique that allows you to cache (store) the system prompt once and reuse it across multiple requests, thereby reducing the number of input tokens.
2. Input Tokens vs Output Tokens:
   • Input tokens: The total number of characters in your prompts and inputs.
   • Output tokens: The number of characters generated by Claude as a response.

Steps to Implement Prompt Caching:

1. Load Dataset: Load the dataset containing TED Talks transcripts.

   python
    
   1df_ted = pd.read_csv('TED_Talk.csv').head(10)[['talk__id','transcript']]

2. Set Cache Control:

   • Mark your system prompt for caching using cache_control.

   python
    
   1self._system = [
   2    {
   3        "type": "text",
   4        "text": get_prompt(),
   5        "cache_control": {"type": "ephemeral"},  # cache breakpoint
   6    }
   7]

3. Invoke Model:

   • Ensure

Read the full article at Towards AI - Medium

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