LLM Evaluation Framework FAQs

Question 1

What metrics does this skill support for LLM testing?

Accepted Answer

The skill supports a wide range of metrics including text generation metrics (BLEU, ROUGE), semantic similarity (BERTScore), classification metrics (F1, Precision/Recall), and RAG-specific retrieval metrics (MRR, NDCG).

Question 2

Does it help with production monitoring?

Accepted Answer

Absolutely. It includes a Regression Detector and an A/B Testing framework to compare new model iterations against baselines and ensure no performance degradation occurs.

Question 3

How does LLM-as-judge work within this skill?

Accepted Answer

It provides implementation patterns for using high-capability models to evaluate outputs from other models through pointwise scoring or pairwise comparisons based on accuracy, helpfulness, and clarity.

Question 4

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

Accepted Answer

Yes, it includes specific evaluation tools for RAG, such as groundedness checks, context relevance metrics, and retrieval performance benchmarks like Precision@K.

Question 5

How do I measure human evaluation quality?

Accepted Answer

The skill includes logic for calculating inter-rater agreement (Cohen's Kappa) to ensure your human annotation team is providing consistent and reliable feedback.

LLM Evaluation Framework

Key Features

Use Cases

LLM Evaluation Framework

Key Features

Use Cases