A comprehensive Python framework for evaluating LLM-extracted structured data against ground truth labels. Supports binary classification, scalar values, and list fields with detailed performance metrics, confidence-based evaluation, and statistical uncertainty quantification via non-parametric bootstrap confidence intervals.
A new package that helps developers integration-test AI and LLM applications by validating structured outputs. It takes a user's test scenario or prompt as input, sends it to an LLM, and uses pattern
Systematic AI evaluation framework that transforms subjective assessment into objective measurement. Reduce research time by 85% while maintaining 95%+ accuracy through multi-LLM validation.
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