Experience Library Update

Purpose

Sample task answers, score outcomes, and derive rules as token priors - achieving continual learning for $18/100 samples vs $10k fine-tune cost, without changing model weights.

When to Use

• Post-task learning and improvement
• Capturing successful patterns
• Learning from failures
• Tool call optimization
• Building domain expertise over time
• Pattern extraction from experience

Core Instructions

Pattern: Experience Capture Loop