What problem does it solve?
This Skill dramatically optimizes AI operational costs by intelligently routing tasks. It sends routine tasks to smaller, cheaper models by default and escalates only low-confidence or complex tasks to larger, more expensive models, achieving 10-30x cost reduction while maintaining accuracy.
Core Features & Use Cases
- Confidence-Based Delegation: Automatically estimates the confidence of a small model's response and escalates to a large model only when confidence is low.
- Cost Optimization: Achieves significant cost savings (up to 30x) by minimizing reliance on expensive large models for routine tasks.
- Faster Learning & Throughput: Enables 87% faster learning and a 5x increase in task throughput by leveraging the speed of smaller models.
- Use Case: For a batch of tasks, a simple math problem ("What is 2+2?") is handled by a small, cheap model with high confidence. A complex philosophical question ("Explain quantum entanglement") is automatically escalated to a large, powerful model, ensuring optimal resource allocation and cost efficiency.
Quick Start
Example: Route a task based on confidence
def route_with_uncertainty(task, confidence_threshold=0.7):
result, confidence = small_model.execute(task) # Try small model first
if confidence >= confidence_threshold:
return result # High confidence: use small model result
else:
result = large_model.execute(task) # Low confidence: escalate to large model
return result
