Prompt caching: 10x cheaper LLM tokens, but how? | ngrok blog
A far more detailed explanation of prompt caching than anyone asked for.
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Technical Blog Post
Summary
Thariq explains how prompt caching powers long‑running agents in Claude Code, describing the ordering of static prompts and tools to maximise cache hits. He shares practical techniques such as using system‑reminder messages, deferring tool loading, and safe compaction to avoid costly cache misses.
Highlights
- Prompt caching reuses prior computation via prefix matching, reducing latency and cost for agents.
- Static system prompts and tools should be placed before dynamic session context to maximize shared prefixes.
- Changing static prompt content or tool definitions causes cache misses and higher expenses.
- Techniques include embedding updates in messages, using stub tools, and exact parent‑prefix matching.
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Context
Audience
AI engineers and product developers building agentic LLM systems
DomainArtificial Intelligence
Formatmicroblog thread with linked article
Accessfree online
Topics
Related
LLM prompt engineeringcache optimization techniquesClaude Code documentationAnthropic API
