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llm-evaluation

Evaluate LLM applications with automated metrics and human feedback.

10|2|Updated Mar 10, 2026
Search Tags:#testing#llm-as-judge#llm#benchmarking#metrics#evaluation
One-click install
npx skills add https://github.com/bugrabilge/bilge-development-kit --skill llm-evaluation-bugrabilge
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/bugrabilge/bilge-development-kit/tree/main/skills-extra/llm-evaluation
Command: npx skills add https://github.com/bugrabilge/bilge-development-kit --skill llm-evaluation-bugrabilge

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires nltk, rouge-score, bert-score, transformers, scipy, detoxify, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the challenge of systematically measuring and improving the quality and performance of Large Language Model (LLM) applications.

Core Features & Use Cases

  • Automated Metrics: Utilize metrics like BLEU, ROUGE, BERTScore, Accuracy, Precision, Recall, F1, MRR, NDCG, etc., for quantitative evaluation.
  • Human Evaluation: Incorporate manual assessment for qualitative aspects like accuracy, coherence, relevance, fluency, safety, and helpfulness.
  • LLM-as-Judge: Leverage powerful LLMs to evaluate outputs from other models, enabling scalable qualitative assessment.
  • A/B Testing & Regression Detection: Implement statistical frameworks to compare model variants and detect performance regressions over time.
  • Use Case: You have developed a new chatbot. Use this Skill to run a suite of automated metrics and a small human evaluation study to ensure its responses are accurate, relevant, and safe before deploying it to production.

Quick Start

Use the llm-evaluation skill to measure the accuracy and BERTScore of your model's responses against 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, and BERTScore against test cases. This approach provides quantitative analysis of model outputs, requiring Python libraries such as nltk, rouge-score, and bert-score.

What is the LLM-as-judge approach for benchmarking model quality?▼

The LLM-as-judge approach leverages powerful LLMs to evaluate outputs from other models. This technique enables scalable qualitative assessment of accuracy, coherence, relevance, and safety without requiring extensive manual human evaluation.

How do I detect performance regressions when updating my LLM application?▼

To detect performance regressions in LLM applications, implement statistical frameworks for A/B testing to compare model variants. This systematic benchmarking detects quality degradation over time before deploying updates to production.

Can I use BERTScore and ROUGE together for comprehensive LLM evaluation?▼

Yes, you can use BERTScore and ROUGE together for comprehensive LLM evaluation. Combining these automated metrics measures both lexical overlap and semantic similarity, providing a quantitative baseline for testing LLM output quality.

Does this LLM evaluation approach support qualitative human feedback assessment?▼

Yes, this LLM evaluation approach supports qualitative human feedback assessment. You can incorporate manual evaluation to measure subjective aspects like fluency, safety, helpfulness, and coherence alongside automated quantitative metrics.

What Python dependencies are needed to benchmark LLM outputs?▼

To benchmark LLM outputs, you need the Python libraries nltk, rouge-score, bert-score, transformers, scipy, and detoxify. These dependencies provide the quantitative and qualitative analysis frameworks required for measuring accuracy and text similarity.

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