"Prompt caching: the cost lever most teams ignore"
On most production pipelines, the majority of input tokens are identical on every single call — and you pay full price for them thousands of times a day. Prompt caching can cut that cost by an order of magnitude, but only if the prompt is architected for it.
Look at the last request your AI system sent to a model provider and ask a simple question: how much of it was new? On most production pipelines the honest answer is not much. The system prompt is the same as the last ten thousand calls. The tool definitions are the same. The few-shot examples are the same. Often a large reference document is the same. The only genuinely new content is the user's input at the end — and you are paying full price to have the model re-process everything above it, every time.
Have an AI feature stuck between demo and production?
The gap — reliability, evals, cost control, the plumbing that keeps it running unattended — is exactly the work I do. If that sounds familiar, a short conversation is usually enough to point you the right way.
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