Context Caching Strategy: Ordering for Cache Hits

<blockquote> <p>Originally published at <a href="https://kalyna.pro/claude-prompt-caching-guide/" rel="noopener noreferrer">kalyna.pro</a></p> </blockquote> <p>If your app sends

<p><strong>TL;DR: We turned on vLLM's prefix cache for our agent workloads at Nexus Labs and watched TTFT drop from 480ms to 110ms on one tenant and stay exactly the same on

<p><em>Originally published at <a href="https://theculprit.ai/blog/anthropic-prompt-caching-90-percent" rel="noopener

<blockquote> <p>Skip the theory rabbit holes. This is the caching knowledge that shows up in system design interviews, code reviews, and the 2 AM production incidents nobody

<h1> Prompt Caching: First vs Repeat Request Costs </h1> <p>Measure prompt-cache cost with a controlled request series: the first request creates or prepares a cached prefix,

<p>LLM API costs add up fast when you're running a production application. If your system prompt is 2,000 tokens and you're processing 10,000 requests a day, you're paying for

<p>Claude's context window holds 200,000 tokens. Gemini's stretches to two million. But response quality starts degrading long before the window fills up. Window size doesn't

<p>The easiest way to make an LLM application look smart is to give the model more context.</p> <p>The harder problem is deciding what context deserves to be there.<br /> While