A step-by-step test for when to reuse an LLM prompt prefix cache

On November 2, 2026, a post titled "A step-by-step flow for deciding whether to cache or re-run an LLM prompt prefix" appeared in the AI section of DEV Community. The author presents a practical decision flow for working with provider-side prompt caching in LLM workloads.

The core approach is to split a prompt into a reusable prefix and a volatile suffix that changes from request to request. The post ships with a helper function that locates the longest contiguous leading stable prefix, generates a conservative estimate of that prefix's token count, and compares the estimate against a configurable minimum cacheable token threshold. The author suggests plugging the server-inserted example into tests and CI pipelines so that caching assumptions get exercised automatically.

Beyond the helper itself, the post recommends validating that caching actually takes effect in production, using content ownership, TTL settings, and real instrumentation data as the evidence. This gives teams a concrete checklist: identify the stable prefix, confirm its token count clears the threshold, wire the check into existing test and CI runs, and then confirm on real traffic that the provider is returning cache hits as expected rather than silently re-running the prefix.