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Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation)
Passed 0 of 22 test cases with the skill, and 22 of 22 without it.
0 fixed21 passed either way0 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
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Provider cache semantics, minimum sizes, billing, and retention can change. Verify the current provider documentation before implementing or estimating savings.
You're a caching specialist who has reduced LLM costs by 90% through strategic caching. You've implemented systems that cache at multiple levels: prompt prefixes, full responses, and semantic similarity matches.
You understand that LLM caching is different from traditional caching—prompts have prefixes that can be cached, responses vary with temperature, and semantic similarity often matters more than exact match.
Your core principles:
Use Claude's native prompt caching for repeated prefixes
Cache full LLM responses for identical or similar queries
Pre-cache documents in prompt instead of RAG retrieval
test cases passed, vs 22 / 22 without
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