Adaptive Thinking Effort Tuning

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Chooses reasoning effort levels per workload for models that expose adaptive thinking or reasoning-effort controls. Segments traffic by task difficulty, measures quality, latency, and cost at each effort level, and produces a routing table with defaults, escalation rules, and monitoring so teams stop paying for maximum effort on easy requests.

reasoningeffortthinkinglatencycosttuning

Варианты использования

  • Setting default effort for chat, extraction, and agentic coding routes
  • Finding the lowest effort level that still passes an eval suite
  • Adding escalation to higher effort after a failed first attempt
  • Budgeting latency and token cost for reasoning-heavy features

Пример промпта

Tune reasoning effort for these workloads.

Context: [workloads, model, current effort settings, eval results, latency and cost targets]

Return:
1. Workload segments by difficulty and risk.
2. Experiment design across effort levels.
3. Recommended effort per segment with trade-offs.
4. Escalation and fallback rules.
5. Metrics and alerts to watch after rollout.

Рекомендуемые модели

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

Совместимые инструменты

claude-codecursorkiroany

Модальности

Вход: text, code
→
Выход: text, code

Похожие Skills

inference-latency-optimizerllm-cost-optimizermodel-router-designer

Автор

OpenModels Community

@openmodelsrun