Prompt caching is a technique that can significantly reduce the cost of using large language models by reusing previously processed input tokens. When a model provider recognizes a repeated prompt prefix, it bills these tokens at a lower cache-read rate instead of the standard input rate. To maximize cache hits, users should structure prompts by placing stable elements like system prompts and tool definitions at the beginning, followed by variable content. Providers like OpenAI, Anthropic, and DeepSeek offer varying cache-read rates and reporting mechanisms for cached tokens, which are crucial for optimizing LLM expenses. AI
IMPACT Reduces operational costs for LLM users by optimizing prompt processing and token usage.
RANK_REASON The item explains a technical optimization for using LLMs, not a new model release or significant industry event.
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