The Prompt Caching Strategy

The prompt caching strategy cost savings can reach 50-90% on your AI bill when implemented correctly. If your system prompt is 2,000 tokens and you send it with every request, caching that prefix means you only pay for it once.

How Prompt Caching Works

Anthropic and OpenAI both support prompt caching. When you send a request with a long system prompt, the provider stores a processed version. The next request with the same prefix skips the processing and charges a reduced rate.

For Claude, cached input tokens cost 90% less than fresh tokens. If your system prompt is 3,000 tokens and you make 100 calls per day, that is 270,000 tokens saved daily. At standard pricing, that adds up fast.

What to Cache

System prompts are the obvious candidate. These rarely change and get sent with every request. Cache them.

Few-shot examples are the next win. If your prompt includes five examples of correct output format, those examples are identical every time. Cache them as part of the prefix.

Context that applies to a session works too. Client profile data, brand guidelines, and project context that stays the same across multiple calls within a workflow.

What Not to Cache

User input obviously changes. Dynamic data like today's date or current metrics should not be cached. Anything that changes between calls needs to go after the cached prefix, not inside it.

Structuring for Cache Hits

Order your prompt components from most stable to least stable. System instructions first, then few-shot examples, then session context, then the dynamic request. This maximizes the cacheable prefix length.

If you reorganize your prompt and put dynamic content in the middle of static content, you break the cache. The prefix must be identical up to the cache boundary.

Measuring the Impact

Track your API costs before and after implementing caching. The Anthropic dashboard shows cache hit rates. Aim for above 80% on your main workflows.

I run caching on all reporting and analysis prompts. The system instructions and client context are the same for every report. Only the fresh data changes. Cache hit rate is consistently above 90%, and the monthly API bill dropped by more than half.

Build These Systems

Ready to implement? These step-by-step tutorials show you exactly how:

• How to Implement Semantic Caching for AI Queries - Cache similar AI queries to avoid redundant API calls and reduce costs by 30%.
• How to Optimize AI Prompts for Speed - Rewrite prompts to get the same quality output in fewer tokens and less time.
• How to Set Up Anthropic Claude with System Prompts - Configure Claude behavior with system prompts for consistent business outputs.