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llm-evaluation

Evaluate LLM applications using automated metrics, human feedback, and benchmarking.

Updated Mar 5, 2026
Search Tags:#testing#quality assurance#llm-as-judge#llm#benchmarking#metrics#evaluation
One-click install
npx skills add https://github.com/Himanshu040604/codex-skills-setup --skill llm-evaluation-himanshu040604
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/Himanshu040604/codex-skills-setup/tree/main/assets/codex/skills/claude-import/skills/plugins/llm-application-dev%40claude-code-workflows/skills/llm-evaluation
Command: npx skills add https://github.com/Himanshu040604/codex-skills-setup --skill llm-evaluation-himanshu040604

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires nltk, rouge-score, bert-score, transformers, detoxify, anthropic, pydantic, scipy, langsmith, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a comprehensive framework for evaluating the performance and quality of Large Language Model (LLM) applications, ensuring reliability and effectiveness.

Core Features & Use Cases

  • Automated Metrics: Implement metrics like BLEU, ROUGE, BERTScore, Accuracy, Precision, Recall, F1, MRR, NDCG, etc.
  • Human Evaluation: Define dimensions and guidelines for manual assessment of LLM outputs.
  • LLM-as-Judge: Utilize powerful LLMs to evaluate responses based on predefined criteria.
  • A/B Testing: Conduct statistical analysis to compare model variants.
  • Regression Testing: Detect performance degradation over time.
  • Use Case: When deploying a new chatbot, use this Skill to systematically test its responses against various metrics and human feedback to ensure it meets quality standards before going live.

Quick Start

Use the llm-evaluation skill to evaluate your model's performance using a suite of automated metrics on a given test dataset.

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 application performance using automated metrics?▼

To evaluate LLM application performance, you can calculate automated metrics like BLEU, ROUGE, BERTScore, Accuracy, Precision, Recall, F1, MRR, and NDCG against a test dataset to measure output quality and reliability.

What is the LLM-as-Judge pattern for evaluating AI responses?▼

The LLM-as-Judge pattern uses a powerful LLM to evaluate responses based on predefined criteria. It supports single output, pairwise, and reference-based assessments to systematically measure AI application quality.

Can I use BERTScore and ROUGE together for benchmarking LLM outputs?▼

Yes, you can use BERTScore and ROUGE together for benchmarking LLM outputs. This approach combines semantic similarity with lexical overlap to provide a comprehensive evaluation of model performance.

How do I conduct A/B testing to compare different LLM model variants?▼

You can conduct A/B testing to compare LLM model variants by applying statistical analysis to evaluation results. This detects performance differences and helps select the best model configuration before deployment.

How do I set up regression testing to detect LLM performance degradation?▼

Regression testing for LLM performance degradation involves systematically running evaluation metrics and LLM-as-Judge assessments over time. This ensures new updates do not negatively impact output quality or reliability.

Does this LLM evaluation framework integrate with LangSmith?▼

Yes, the framework integrates with LangSmith alongside libraries like detoxify, transformers, and scipy. These dependencies support toxicity detection, model inference, and statistical analysis for robust evaluation.

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