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llm-evaluation

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

Updated Mar 2, 2025
Search Tags:#llm-as-judge#benchmarking#llm-evaluation#human-evaluation#nlp-evaluation#automated-metrics
One-click install
npx skills add https://github.com/apassuello/multimodal_insight_engine --skill llm-evaluation-apassuello
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/apassuello/multimodal_insight_engine/tree/main/.claude/skills/llm-evaluation
Command: npx skills add https://github.com/apassuello/multimodal_insight_engine --skill llm-evaluation-apassuello

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a structured framework to evaluate LLM outputs using automated metrics, human feedback, and benchmarking to reduce risk and accelerate product decisions.

Core Features & Use Cases

  • Automated Metrics: Compute standard text-generation and classification metrics (e.g., BLEU, ROUGE, METEOR, BERTScore, per-example and aggregate reports) to quantify performance.
  • Human Evaluation: Define dimensions (Accuracy, Relevance, Coherence, Fluency, Safety, Helpfulness) and collect structured human judgments for ground truth comparisons.
  • LLM-as-Judge: Leverage higher-capacity models to judge other model outputs using pointwise or pairwise evaluation with JSON-formatted results.
  • Benchmarking & Reproducibility: Integrate with a suite of tests, baselines, and versioned datasets to track progress over time.
  • Use Case: Ideal for validating chatbots, QA systems, summarization pipelines, and content filtering in a multimodal or language-centric workflow.

Quick Start

Prepare evaluation tasks, datasets, and prompts. Run the evaluation suite against your model using the provided API, then review metrics and human judgments to identify improvements.

  • Run: python -m llm_eval --suite default --model your-model
  • Inspect: python -m llm_eval --report all

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?▼

To evaluate LLM outputs using automated metrics like BLEU and ROUGE, you can run an evaluation suite that computes text-generation and classification metrics, generating per-example and aggregate performance reports.

What is LLM-as-judge evaluation and how does it work for benchmarking models?▼

LLM-as-judge evaluation works by leveraging higher-capacity models to judge other model outputs using pointwise or pairwise evaluation, returning structured JSON-formatted results for comparative benchmarking.

Can I use human evaluation dimensions for chatbot and summarization pipeline validation?▼

Yes, you can use structured human evaluation by defining dimensions such as Accuracy, Relevance, Coherence, Fluency, Safety, and Helpfulness to collect human judgments for ground truth comparisons in chatbot and summarization validation.

Does this LLM evaluation framework support reproducible benchmarking with versioned datasets?▼

Yes, the framework supports reproducible benchmarking by integrating a suite of tests, baselines, and versioned datasets to track model progress over time across generation, classification, and retrieval workflows.

What's the best way to set up an EvaluationSuite for automated metric computation?▼

The best way to set up an EvaluationSuite for automated metric computation is to prepare your evaluation tasks, datasets, and prompts, then run the suite against your model via the command line API to review metrics and judgments.

When should I not use automated metrics for NLP evaluation?▼

You should not rely solely on automated metrics for NLP evaluation when assessing nuanced dimensions like Safety or Helpfulness, where structured human evaluation or LLM-as-judge approaches provide necessary qualitative validation.

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