Prompt caching is a technique to reduce costs and latency when interacting with LLMs like those from OpenAI and Anthropic. By saving intermediate computations for stable parts of a prompt, providers can reuse this work for subsequent requests that share the same prefix. To effectively utilize prompt caching, developers must ensure that unchanging elements such as tool definitions, system prompts, and reference documents appear at the beginning of the prompt, followed by dynamic content like user messages. Incorrect ordering, such as placing dynamic elements at the start, can negate the benefits of caching, leading to higher costs and slower response times. AI
IMPACT Optimizing prompt structure can significantly reduce inference costs and latency for LLM applications.
RANK_REASON The item discusses a technical optimization for using LLM APIs, not a new model release or core research.
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