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llm-evaluation

Measures LLM-based application performance via automated metrics, human judgments, and benchmarking.

Updated Dec 10, 2024
Search Tags:#llm#benchmarking#metrics#evaluation#nlp#ab-testing#human-evaluation
One-click install
npx skills add https://github.com/melikhanmutlu/web_ar --skill llm-evaluation-melikhanmutlu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/melikhanmutlu/web_ar/tree/main/skills-extra/llm-evaluation
Command: npx skills add https://github.com/melikhanmutlu/web_ar --skill llm-evaluation-melikhanmutlu

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill helps teams quantify and compare the performance of LLM-based systems using automated metrics, human judgments, and benchmarking.

Core Features & Use Cases

  • Automated evaluation across generation quality, factuality, and safety using established metrics.
  • Human-in-the-loop dimensions such as accuracy, relevance, coherence, fluency, and safety assessments.
  • LLM-as-Judge and Pairwise/Reference-based evaluation patterns to guide model improvement and selection.
  • A/B testing and baseline tracking to measure progress over time against defined baselines.

Quick Start

Run the evaluation suite on your model with a representative test set to generate a baseline of metrics.

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 and human judgments?▼

LLM evaluation combines automated metrics for generation quality, factuality, and safety with human-in-the-loop assessments like accuracy, relevance, and coherence. Apply these dimensions to model comparisons, prompt refinements, and regression checks to generate reproducible performance reports.

Can I use LLM-as-Judge and pairwise comparison for model selection?▼

LLM-as-Judge and pairwise, reference-based evaluation patterns guide model improvement and selection. These methods compare model outputs against baselines or each other, helping teams identify the best performing LLM for specific generation and safety tasks.

How do I run A/B testing and baseline tracking for LLM applications?▼

A/B testing and baseline tracking measure LLM progress over time against defined baselines. Run the evaluation suite on your model with a representative test set to generate baseline metrics, then compare subsequent model versions or prompt changes against this initial performance benchmark.

How do I integrate LLM evaluation orchestration into existing ML pipelines?▼

LLM evaluation supports end-to-end orchestration and integration with existing ML pipelines. This enables automated regression checks across tasks like generation quality, factuality, and safety, producing reproducible reporting metrics directly within your continuous integration and deployment workflows.

What specific dimensions does human evaluation cover for LLM outputs?▼

Human evaluation covers accuracy, relevance, coherence, fluency, and safety assessments. These human-in-the-loop dimensions complement automated metrics to provide a robust, multi-dimensional view of LLM application performance during benchmarking and model comparisons.

When should I use automated metrics versus human evaluation for benchmarking LLMs?▼

Automated metrics provide scalable, reproducible benchmarking for generation quality, factuality, and safety, while human evaluation adds nuanced judgments on accuracy and coherence. Combining both establishes robust baseline tracking and comprehensive regression checks for LLM applications.

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