Prompt Caching Strategy

СреднийopsМинимальный контекст: 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-cachingcostlatencycontextllm-ops

Варианты использования

  • 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

Пример промпта

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.

Рекомендуемые модели

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

Совместимые инструменты

claude-codecursorkiroany

Модальности

Вход: text, code
→
Выход: text, code

Похожие Skills

llm-cost-optimizermodel-context-budgetinginference-latency-optimizer

Автор

OpenModels Community

@openmodelsrun