Prompt Caching and Context Cost Optimization
Why input tokens, not output tokens, drive the bill — and how cache-aware prompt design, context trimming, and model routing cut it by an order of magnitude
A mechanism-first look at prompt caching as a cost-engineering discipline: how prefix caching and KV-cache reuse actually work under the hood, why context engineering decisions (prompt layout, history trimming, retrieval top-k) are also cache-hit-rate decisions, when model routing and cascades beat a single big model, and a full worked example combining caching, trimming, and routing to cut an app's token bill roughly 10x. Written as an AI Engineer interview reference with real comparison tables and worked arithmetic.
Practice questions (5)
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The Cache Stopped Helping and Nobody Changed the Prompt Text
Intermediate · Free -
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Should This Low-Traffic Endpoint Even Use Prompt Caching?
Intermediate -
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Trimming Broke the Cache the Team Was Relying On
Intermediate -
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Design a Routing Cascade and Justify the Threshold
Intermediate -
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Propose a Cost-Reduction Plan for a Runaway Agent Loop
Intermediate