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llm-evaluation

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

Updated Sep 10, 2025
Search Tags:#testing#llm#benchmarking#metrics#evaluation#nlp#ai quality
One-click install
npx skills add https://github.com/cuoreinpace/bdeornelas.github.io --skill llm-evaluation-cuoreinpace
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/cuoreinpace/bdeornelas.github.io/tree/main/plugins/llm-application-dev/skills/llm-evaluation
Command: npx skills add https://github.com/cuoreinpace/bdeornelas.github.io --skill llm-evaluation-cuoreinpace

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a comprehensive framework for evaluating the performance of Large Language Models (LLMs) and LLM-powered applications, ensuring quality, reliability, and safety before and after deployment.

Core Features & Use Cases

  • Automated Metrics: Utilize metrics like BLEU, ROUGE, BERTScore, Accuracy, Precision, Recall, F1, MRR, and NDCG for quantitative assessment.
  • Human Evaluation: Define dimensions (Accuracy, Coherence, Relevance, Fluency, Safety, Helpfulness) and guidelines for manual quality checks.
  • LLM-as-Judge: Leverage powerful LLMs to evaluate outputs based on predefined criteria.
  • A/B Testing & Regression Testing: Implement statistical analysis for comparing model variants and detecting performance degradation.
  • Benchmarking: Run models against standard datasets to establish performance baselines.
  • Use Case: Before deploying a new chatbot, use this Skill to run a suite of automated metrics and a small human evaluation study to confirm it meets accuracy and helpfulness targets, and then set up regression tests to monitor performance in production.

Quick Start

Use the llm-evaluation skill to evaluate your model using accuracy and BLEU metrics on 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?▼

You can evaluate LLM performance by calculating automated metrics like BLEU, ROUGE, BERTScore, and F1 on test datasets to establish quantitative baselines. This framework systematically measures model quality and generates scores for comparative analysis.

What is the best way to test LLM quality before deployment?▼

The best way to test LLM quality before deployment is combining automated metrics, LLM-as-Judge evaluations, and human evaluation studies. This framework measures accuracy, coherence, relevance, and safety to ensure your application meets reliability targets.

How does LLM-as-Judge work for evaluating AI outputs?▼

LLM-as-Judge evaluates AI outputs by leveraging powerful LLMs to assess responses based on predefined criteria like accuracy and helpfulness. This approach automates qualitative evaluation, reducing manual overhead while maintaining rigorous quality measurement standards.

Can I run regression testing to detect LLM performance degradation in production?▼

Yes, you can run regression testing to detect LLM performance degradation by implementing continuous statistical analysis on production outputs. This framework monitors metrics over time, automatically flagging comparative performance drops against established baselines.

Does this LLM evaluation framework support A/B testing different model variants?▼

Yes, this framework supports A/B testing different model variants by implementing statistical analysis for comparative analysis. It systematically measures performance differences across models, helping you identify the best-performing configuration for your LLM application.

What dimensions should I use for human evaluation of LLM outputs?▼

For human evaluation of LLM outputs, use dimensions including Accuracy, Coherence, Relevance, Fluency, Safety, and Helpfulness. This framework provides guidelines for manual quality checks, ensuring systematic qualitative assessment alongside automated quantitative metrics.

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