LLM Prompt-Caching Structure OptimizerPRO
Structure complex prompt contexts into optimized static-cached and dynamic-mutation zones to maximize cache hits.
CACHE TARGET
MUTATION CONTEXT
0
Static Tokens
0
Dynamic Tokens
0%
Est. Savings
Input Transaction Cost Analysis
Standard Input Cost (No Caching)$0.00000
Optimized Architecture Cost$0.00000
manifest-payload-compiled.json
[
{
"role": "system",
"content": [
{
"type": "text",
"text": "",
"cache_control": {
"type": "ephemeral"
}
}
]
},
{
"role": "user",
"content": ""
}
]Instructions
- 1
Choose your target model provider (Anthropic or OpenAI).
- 2
Paste your system instructions and invariant schemas into the Static zone.
- 3
Enter your user queries or variable inputs into the Dynamic zone.
- 4
Inspect token usage, cost analysis, and download the compiled JSON structure.
Frequently Asked Questions
By separating static instructions (which don't change frequently) from dynamic queries, LLM providers can cache the static part, reducing API costs and latency.
Anthropic Claude requires a minimum of 1,024 tokens in the cached block to trigger caching benefits. A warning will appear if you are below this limit.