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llm-evaluation

Automate LLM evaluation with BLEU, ROUGE, BERTScore, and human feedback.

4|Updated Mar 3, 2026
Search Tags:#benchmarking#metrics#llm-evaluation#llm-judge#evaluation-framework#human-feedback
One-click install
npx skills add https://github.com/AI-Foundry-Core/ril-agents --skill llm-evaluation-ai-foundry-core
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/AI-Foundry-Core/ril-agents/tree/main/plugins/llm-application-dev/skills/llm-evaluation
Command: npx skills add https://github.com/AI-Foundry-Core/ril-agents --skill llm-evaluation-ai-foundry-core

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automate and standardize evaluation of LLM-powered applications across automated metrics, human feedback, and benchmarking to accelerate reliability and governance.

Core Features & Use Cases

  • Automated Metrics: BLEU, ROUGE, METEOR, BERTScore, and perplexity-based assessments for text generation.
  • Human Evaluation & LLM-Judging: Structured human scores and LLM-based judgments (A/B style, reference-based, and pointwise scoring) to ensure quality and safety.
  • Reference-Based & Regression Thinking: Tools for comparing outputs against gold references, tracking regressions, and validating improvements over time.

Quick Start

Provide a ready-to-use evaluation pipeline that combines automated metrics, human feedback, and LLM-based judging.

Frequently Asked Questions about llm-evaluation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate LLM evaluation with BLEU, ROUGE, and BERTScore metrics?▼

Automate LLM evaluation by applying automated metrics like BLEU, ROUGE, METEOR, and BERTScore to standardize text generation quality assessments. This provides a ready-to-use pipeline for consistent performance testing across development and production environments.

What is LLM-based judging and how does it work for evaluating model outputs?▼

LLM-based judging evaluates outputs using A/B style pairwise comparisons, reference-based scoring, and pointwise assessments. It acts as an automated judge to ensure quality and safety without requiring constant manual human review.

How do I track LLM regressions and compare outputs against gold references?▼

Track LLM regressions by comparing generated outputs against gold references using reference-based evaluation methods. This validates improvements over time and ensures model updates do not degrade existing performance.

Can I combine automated metrics with human feedback for LLM quality assurance?▼

Combine automated metrics with human feedback for comprehensive LLM quality assurance. Structured human scores integrate with automated evaluations to satisfy governance requirements and validate ongoing model performance.

Does this evaluation framework support benchmarking across different LLM models?▼

This evaluation framework supports benchmarking across different LLM models to accelerate reliability. It standardizes performance testing and validation in research and production environments using automated metrics and LLM-based judging.

What is the best way to set up a standardized evaluation pipeline for LLM applications?▼

The best way to set up a standardized LLM evaluation pipeline is combining automated metrics, human feedback, and LLM-based judging. This approach standardizes quality assurance and governance across various application environments.

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