Posts tagged Prompt Caching — AI × software engineering, made practical.
You'll learn how prompt caching makes time-to-first-token depend on the order of your prompt, how to find the lines that break the cache in your agent harness, and how to order the prompt so the cache keeps hitting on every turn.
You will learn how to measure where tokens go in an orchestrator-plus-sub-agents fleet, and how to cut re-read context with cache-friendly prompt prefixes, a shared context ledger and decision-based handoffs.
You will learn how to model, measure and reduce the tokens each agent spends reading, validating and summarising the same shared data, and how to price that spend next to your warehouse bill.
You'll learn how to measure prompt cache hits in a coding agent harness, and how to fix the things that quietly break caching: volatile values in the system prompt, shifting tool lists, shared CLAUDE.md edits, aggressive compaction and badly placed breakpoints.
You'll learn how prompt caching keys on an exact prefix, why a one-line edit to a shared tool schema silently invalidates it across every agent that imports it, and what to monitor so it doesn't cost you a week of spend before anyone notices.
The cache you turned on is working exactly as designed — it's just guarding the one line item on your invoice that was never the problem.
Your CDN dashboard and your origin server are both telling the truth about the same traffic — which is exactly how a 95% hit ratio and rising origin CPU coexist, and the same trick is now hiding inside your LLM inference stack.