skills.rest
JNZader
JNZaderCOMMUNITY
Explore all skills by this author →
GitHub

llm-evaluation

Automate LLM output evaluation with BLEU, ROUGE, BERTScore, and judge prompts.

Updated Mar 15, 2026
Search Tags:#rag#metrics#evaluation#prompt#ab-testing#llm-evaluation#langsmith
One-click install
npx skills add https://github.com/JNZader/javi-ai --skill llm-evaluation-jnzader
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/JNZader/javi-ai/tree/main/own/skills/llm-evaluation
Command: npx skills add https://github.com/JNZader/javi-ai --skill llm-evaluation-jnzader

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Never ship LLM features without evaluation. An untested prompt is a broken prompt you haven't found yet.

Core Features & Use Cases

  • Automated Metrics: BLEU, ROUGE, BERTScore, LLM-as-Judge patterns, Pairwise and Reference-based evaluations, and RAG evaluation workflows.
  • Evaluation Pipelines: Single-output, pairwise, reference-based, and RAG-based evaluations; AB testing and regression detection to validate prompt and model changes.
  • Integration & Best Practices: LangSmith integration examples and guidance for building robust evaluation datasets and repeatable experiments.

Quick Start

Run an evaluation pipeline against your current prompts to establish a baseline.

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 automatically for prompt optimization?▼

Automate LLM evaluation by applying standardized metrics like BLEU, ROUGE, and BERTScore alongside LLM-as-Judge patterns to measure and improve prompt outputs. This establishes a baseline for prompt optimization and quality assurance.

What metrics are used for RAG evaluation pipelines?▼

RAG evaluation pipelines use automated metrics including ROUGE, BERTScore, and LLM-as-Judge patterns to assess retrieval and generation quality. These metrics support single-output, pairwise, and reference-based evaluations for regression detection.

Can I run AB testing to compare different LLM models?▼

Yes, you can run AB testing and regression checks to validate prompt and model changes. Pairwise evaluation workflows compare outputs from different models side-by-side to identify quality improvements.

Does LangSmith integration support LLM evaluation workflows?▼

LangSmith integration is supported for building robust evaluation datasets and repeatable experiments. It connects with Python tooling like rouge_score, nltk, and bert_score to streamline the evaluation pipeline.

What is the best way to set up reference-based evaluation for LLMs?▼

Reference-based evaluation compares generated LLM outputs against a ground truth dataset using metrics like BLEU, ROUGE, and BERTScore. This approach validates quality changes during regression checks and AB tests.

When do I need LLM-as-Judge patterns for evaluating model outputs?▼

Use LLM-as-Judge patterns when standard lexical metrics like ROUGE are insufficient for measuring semantic quality. This approach leverages automated judge prompts to evaluate complex outputs within comprehensive evaluation pipelines.

Related Skills

Explore More Data & Analytics Skills→
ResearAIResearAI
3.3k

baseline

Standardize a reference object for quest evaluation with plan and checklist requirements.

Community
Advanced
robit-manrobit-man

regression-cicd-hooks

Automate regression testing in CI/CD pipelines with baseline comparison and merge blocking.

Community
Advanced
jxtngxjxtngx
2

langsmith-dataset

Convert LangSmith trace logs into structured JSON evaluation datasets.

Community
Intermediate
ContextJet-aiContextJet-ai
29

add-llm-evals

Add offline and online evaluation scoring to LLM and agent applications.

Official
Advanced