Risk Metric Computation And Monitoring

via “research-backed metric library with 50+ implementations”

LLM evaluation framework — 14+ metrics, faithfulness/hallucination detection, Pytest integration.

Unique: Implements metrics using a three-tier approach: (1) LLM-as-judge via G-Eval prompts with structured output parsing, (2) statistical methods (ROUGE, BERTScore) for reference-based evaluation, (3) specialized NLP models for toxicity/bias; this hybrid approach allows choosing the right evaluation method per metric rather than forcing all metrics through a single paradigm

vs others: Broader metric coverage (50+ vs Ragas' 10-15) and RAG-specific metrics (contextual recall, context precision) make it more suitable for evaluating retrieval-augmented systems than general-purpose LLM evaluation frameworks