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llm-evaluation

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

Updated Feb 3, 2026
Search Tags:#benchmarking#metrics#evaluation#nlp#llm-evaluation#llm-judge#human-evaluation
One-click install
npx skills add https://github.com/leonardoteodoroo/amino-advanced --skill llm-evaluation-leonardoteodoroo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/leonardoteodoroo/amino-advanced/tree/main/.agent/skills/llm-evaluation
Command: npx skills add https://github.com/leonardoteodoroo/amino-advanced --skill llm-evaluation-leonardoteodoroo

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a structured framework to evaluate LLM outputs using automated metrics, human feedback, and benchmarking across development and deployment.

Core Features & Use Cases

  • Automated Metrics: BLEU, ROUGE, METEOR, BERTScore, perplexity for text generation, plus classification and retrieval metrics to cover different output types.
  • Human Evaluation: Dimensions like accuracy, coherence, relevance, fluency, safety, and usefulness with scalable annotation patterns.
  • LLM-as-Judge & Benchmarking: Techniques for pointwise, pairwise, and reference-based evaluation, plus regression testing and A/B comparisons. Ideal for model comparison, prompt engineering validation, and production readiness.

Quick Start

Run the evaluation suite against your model by importing EvaluationSuite and supplying a list 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 outputs using automated metrics like BLEU and ROUGE?▼

You can evaluate LLM outputs using automated metrics by running an evaluation suite that calculates BLEU, ROUGE, METEOR, BERTScore, and perplexity for text generation, plus classification and retrieval metrics to cover different output types systematically.

What is the LLM-as-Judge pattern for benchmarking language models?▼

The LLM-as-Judge pattern for benchmarking involves using language models to evaluate outputs through pointwise, pairwise, and reference-based techniques, enabling systematic comparison, regression detection, and A/B testing for model development and production validation.

How do I set up human evaluation frameworks for LLM accuracy and relevance?▼

You set up human evaluation frameworks for LLM accuracy and relevance by defining annotation dimensions like coherence, fluency, safety, and usefulness, then applying scalable annotation patterns to collect structured human feedback on model outputs.

Can I use this evaluation tooling for prompt engineering validation and production readiness?▼

Yes, you can use this evaluation tooling for prompt engineering validation and production readiness by running regression testing and A/B comparisons against test cases to ensure quality control before deployment.

What's the best way to compare LLM performance across different prompts and models?▼

The best way to compare LLM performance across prompts and models is by importing an EvaluationSuite, supplying a list of test cases, and applying pointwise, pairwise, and reference-based evaluation techniques to detect regressions and validate quality.

Do I need external dependencies to run LLM evaluation metrics and benchmarks?▼

No external dependencies are required to run LLM evaluation metrics and benchmarks, as the evaluation suite operates independently to calculate automated metrics, facilitate human evaluation, and execute LLM-as-Judge patterns for comprehensive output assessment.

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