Checklist for effective LLM prompt caching (medium.com)

APIs for large language models commonly support prompt caching, and using it well can cut inference cost and latency significantly. This checklist condenses practical rules to boost cache hit rates: place the static portion of a prompt at the beginning (caches are generally based on prefix matches), order few‑shot or dynamic examples by least‑recently‑used so frequently changing examples sit at the end, and ensure requests go to a consistent shard (for OpenAI, use a stable prompt_cache_key and unique caller prefixes to avoid cache collisions and overflow). Operational monitoring and measurement are essential: track cached_tokens (e.g., usage.prompt_tokens_details for OpenAI) to compute per‑caller hit rates and identify regressions. The implications for system design are straightforward but powerful—structure prompts to maximize shared prefixes, manage example churn to avoid invalidating large portions of the cache, and shard consistently to concentrate hits. Together these practices yield measurable latency and cost savings and make few‑shot prompting and dynamic examples far more efficient in production.