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llm-evaluation

Implement automated metrics and human feedback for LLM evaluation.

Updated Aug 27, 2026
Search Tags:#testing#llm#benchmarking#metrics#evaluation#ai quality
One-click install
npx skills add https://github.com/TriNgo0108/z-command --skill llm-evaluation-tringo0108
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/TriNgo0108/z-command/tree/main/templates/skills/llm-evaluation
Command: npx skills add https://github.com/TriNgo0108/z-command --skill llm-evaluation-tringo0108

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires nltk, rouge-score, bert-score, transformers, detoxify, openai, scipy, numpy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the critical need for robust and systematic evaluation of Large Language Models (LLMs) and their applications, ensuring performance, quality, and reliability.

Core Features & Use Cases

  • Automated Metrics: Implement standard metrics like BLEU, ROUGE, BERTScore, Accuracy, Precision, Recall, F1, MRR, NDCG, etc.
  • Human Evaluation: Define frameworks for manual assessment across dimensions like accuracy, coherence, relevance, fluency, safety, and helpfulness.
  • LLM-as-Judge: Utilize powerful LLMs to evaluate outputs, either pointwise or pairwise.
  • A/B Testing & Regression: Conduct statistical analysis for comparing model variants and detect performance regressions.
  • Use Case: You've developed a new chatbot. Use this Skill to systematically measure its factual accuracy, helpfulness, and compare it against a previous version using A/B testing before deploying it to users.

Quick Start

Use the llm-evaluation skill to evaluate your model's accuracy and BLEU score against a set of test cases.

Frequently Asked Questions about llm-evaluation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I evaluate LLM performance using automated metrics?▼

Evaluate LLM performance by computing automated metrics like BLEU, ROUGE, and BERTScore against test cases. This approach systematically measures text generation quality and factual accuracy to ensure model reliability before deployment.

What is the best way to compare two LLM model variants?▼

Compare LLM model variants by conducting A/B testing and statistical regression analysis. This process evaluates performance differences using metrics like MRR and NDCG to determine which model performs better before release.

Can I use an LLM as a judge to evaluate AI application quality?▼

Yes, you can use an LLM as a judge to evaluate AI application quality. This technique utilizes powerful models to assess outputs pointwise or pairwise across dimensions like coherence, relevance, and helpfulness.

How do I measure toxicity and groundedness in LLM outputs?▼

Measure toxicity and groundedness in LLM outputs by applying custom evaluation metrics. This process leverages libraries like detoxify and transformers to detect unsafe content and verify factual grounding.

Does this LLM evaluation framework support both human evaluation and automated benchmarking?▼

Yes, this LLM evaluation framework supports both human evaluation and automated benchmarking. It defines manual assessment frameworks for safety and fluency while implementing standard metrics for comprehensive performance testing.

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