prompt-caching
Optimize LLM Costs with Prompt Caching
Reduce LLM API costs by up to 90% through strategic prompt caching implementation including Anthropic native caching, response caching, and CAG patterns.
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測試它
正在使用「prompt-caching」。 How do I reduce my LLM API costs?
預期結果:
You can reduce LLM API costs by implementing prompt caching. The main strategies are: 1) Use Anthropic's native prompt caching for repeated system prompts - this caches the prompt prefix so you only pay for new tokens. 2) Implement response caching for identical queries - store hash of prompt and return cached response. 3) Use Cache Augmented Generation (CAG) to pre-cache documents instead of doing RAG retrieval each time.
正在使用「prompt-caching」。 Why is my cache not working?
預期結果:
Common reasons prompt caching fails: 1) Using non-zero temperature - cached responses vary. 2) Changing prompt prefixes frequently - cache depends on exact prefix match. 3) Not using the cache-control header properly. 4) Exceeding the cache TTL (typically 5 minutes for Anthropic).
安全審計
安全All static findings are false positives. The skill is a markdown documentation file with no executable code. The external_commands detections are markdown backticks used for code formatting. The weak cryptographic algorithm detections are misinterpretations of YAML frontmatter content. No actual security risks present.
品質評分
你能建構什麼
Reduce API Costs for Production Applications
Implement prompt caching to dramatically reduce LLM API costs in production systems with repeated context
Optimize Long-Running Conversations
Use caching to maintain conversation context without incurring full context costs on each message
Improve Response Latency
Leverage cached responses to achieve faster response times for repeated queries
試試這些提示
How do I set up prompt caching with Claude API? Show me the basic implementation steps.
Design a response caching strategy for a Q&A system that handles similar user queries. Include cache key design and invalidation logic.
Explain Cache Augmented Generation (CAG) and provide a Python implementation pattern for pre-caching documents.
What are the best practices for cache invalidation in LLM applications? Include time-based and event-based strategies.
最佳實務
- Structure prompts with static prefixes that remain consistent across requests
- Use zero temperature when caching responses for exact matches
- Implement proper cache invalidation with time-based or event-based triggers
- Monitor cache hit rates and optimize prefix structure accordingly
避免
- Caching responses with high temperature settings - outputs will vary and cached data becomes useless
- Caching without any invalidation strategy - stale data leads to incorrect responses
- Caching everything indiscriminately - increases latency on cache misses without proportional benefit