Prompt Caching: Just do it (kreidemann.com)

A new article introduces a decision framework for prompt caching in large language model (LLM) applications, underscoring its importance in optimizing performance and reducing costs. Prompt caching allows LLMs to skip reprocessing unchanged input tokens during the prefill phase, thus significantly decreasing computational overhead and enhancing responsiveness, particularly in multi-turn conversations and agentic workflows. The framework details when to cache various components of prompts while balancing potential security risks, especially concerning timing side channel attacks that could expose sensitive user data. The article emphasizes that while prompt caching generally yields substantial benefits, developers must be cautious when handling sensitive information. By adding a unique identifier to the start of the messages array, users can prevent unauthorized access to cached data through timing attacks. This method preserves caching efficiency for shared components while isolating sensitive user data, making prompt caching a critical optimization strategy in developing robust and efficient AI applications.