Prompt Caching Strategy

Name: OpenModels
Creator: OpenModels
License: https://github.com/openmodelsrun/openmodels

IntermediaopsContexto mínimo: 32K

Designs a prompt caching strategy for LLM applications. Restructures prompts so stable content such as system instructions, tool definitions, and reference documents forms a cacheable prefix, places cache breakpoints, estimates break-even from cache-write and cache-read prices and hit rates, and defines monitoring for cache effectiveness.

prompt-caching cost latency context llm-ops

Casos de uso

Cutting input cost for agents with long system prompts and tool lists
Ordering prompt sections so the cacheable prefix stays stable
Estimating savings from cache-read pricing at a given hit rate
Diagnosing low cache hit rates in production

Prompt de ejemplo

Design prompt caching for this application.

Context: [prompt structure, tools, documents, request volume, provider and prices]

Return:
1. Which sections are stable, semi-stable, and volatile.
2. Reordered prompt layout and cache breakpoints.
3. Break-even and savings estimate with assumptions.
4. Cache invalidation risks and TTL considerations.
5. Metrics to track hit rate and cost per request.

Modelos recomendados

claude-opus-5-5 gpt-6-astra gpt-6-1-sol

Herramientas compatibles

claude-codecursorkiroany

Modalidades

Entrada: text, code

→

Salida: text, code

Skills relacionadas

llm-cost-optimizer model-context-budgeting inference-latency-optimizer

Autor

OpenModels Community

@openmodelsrun