Why Care About Prompt Caching in LLMs?

Prompt caching is a technique that reuses previously computed token representations across LLM requests, reducing both cost and latency significantly. The post explains LLM inference stages (pre-fill and decoding), how KV caching works within a single response, and how prompt caching extends this across different users and sessions. Key practical rules include placing static content like system prompts at the start of the prompt so prefixes match. A Python example using the OpenAI API demonstrates 99% token savings when a large shared prefix is reused. OpenAI requires a minimum of 1,024 tokens to activate caching, making it most beneficial for high-traffic AI applications.