experience-library

Continual learning through pattern extraction captures task outcomes and derives token priors, achieving learning improvements for $18 per 100 samples versus $10,000 for traditional fine-tuning. This approach learns from successful patterns and failures without updating model weights.

Token priors store reusable patterns extracted from task outcomes, injected as context for similar future tasks. The system scores performance, identifies successful approaches, and retrieves relevant rules by task type without requiring model weight updates.

Pattern extraction automatically identifies and stores successful approaches used in specific task types. After a successful REST API call with retry logic, that pattern is captured and reused the next time you execute similar integration tasks.

Experience-based learning maintains non-weighted context of tool performance patterns by domain, enabling lightweight interfaces to retrieve and apply the most effective tools and approaches for each task category without expensive retraining.

The system requires scoring outcomes to extract reusable patterns, but this scoring integrates into the task completion workflow. Results are stored as token priors and automatically retrieved for similar future tasks.