LLM Evaluation Framework FAQs

Question 1

How does the 'LLM-as-judge' pattern work?

Accepted Answer

It utilizes high-capability models to evaluate the outputs of other models or prompts based on specific dimensions like accuracy, helpfulness, and coherence, providing both scores and reasoning.

Question 2

How does it handle performance regressions?

Accepted Answer

It includes a RegressionDetector that compares current evaluation results against a baseline, flagging significant drops in performance based on a configurable sensitivity threshold.

Question 3

Does it support statistical significance testing?

Accepted Answer

Yes, the skill provides an A/B testing framework that calculates p-values and Cohen's d effect sizes to determine if model improvements are statistically significant.

Question 4

What metrics are supported by this evaluation skill?

Accepted Answer

The skill supports a wide range of metrics including BLEU, ROUGE, METEOR, BERTScore, and classification metrics like F1-score, as well as RAG-specific metrics like MRR and NDCG.

Question 5

Can I use this for RAG (Retrieval-Augmented Generation) apps?

Accepted Answer

Yes, it includes specific patterns for measuring retrieval quality (Precision@K) and checking if generated responses are grounded in the provided context.

LLM Evaluation Framework

Key Features

Use Cases

LLM Evaluation Framework

Key Features

Use Cases