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MayaDispeler
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ai-evaluation-framework

Evaluate LLMs, RAG pipelines, and AI features against defined standards.

3|Updated Apr 14, 2026
Search Tags:#llm evaluation#ai evaluation#ai metrics#ai benchmarking#rag pipeline evaluation
One-click install
npx skills add https://github.com/MayaDispeler/TheOrqestra --skill ai-evaluation-framework
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-evaluation-framework
Source: https://github.com/MayaDispeler/TheOrqestra/tree/main/skills/ai-evaluation-framework
Command: npx skills add https://github.com/MayaDispeler/TheOrqestra --skill ai-evaluation-framework

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a comprehensive framework for evaluating AI systems, ensuring accurate and reliable assessments for LLMs, RAG pipelines, and other AI features.

Core Features & Use Cases

  • Expert Reference: Offers a detailed guide to evaluating AI systems, with clear standards and best practices.
  • Non-Negotiable Standards: Defines critical evaluation principles such as versioned datasets and human validation.
  • Decision Rules: Provides specific rules for evaluating RAG systems, LLM-as-judge models, and benchmarking new tasks.
  • Mental Models: Explains the Evaluation Pyramid and Coverage Matrix for a structured evaluation approach.
  • Vocabulary: Defines key terms used in AI evaluation.
  • Common Mistakes: Lists common evaluation mistakes and how to avoid them.
  • Good vs. Bad Output: Illustrates the difference between good and bad evaluation reports.
  • Evaluation Checklist: A comprehensive checklist for evaluating AI systems.

Quick Start

Use the ai-evaluation-framework skill to evaluate a new LLM system by following the evaluation pyramid and checking all points in the evaluation checklist.

Frequently Asked Questions about ai-evaluation-framework

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

FAQPage Schema
How do I evaluate AI systems in production using structured benchmarks?▼

To evaluate AI systems in production, apply an evaluation pyramid framework using versioned datasets and human validation. This provides precise benchmarks and non-negotiable standards for reliable LLM and RAG pipeline assessments.

What metrics should I use for RAG pipeline evaluation?▼

RAG pipeline evaluation requires specific decision rules for retrieval and generation tasks. Use a coverage matrix to structure your approach, ensuring both components meet non-negotiable standards like human validation.

Can I use LLM-as-judge models for benchmarking new AI tasks?▼

Yes, you can use LLM-as-judge models for benchmarking new tasks by applying specific decision rules. You must maintain non-negotiable standards like versioned datasets and human validation to ensure accurate assessments.

What are common mistakes in LLM evaluation and how do I avoid them?▼

Common LLM evaluation mistakes include ignoring human validation and failing to use versioned datasets. Avoid these by following a comprehensive evaluation checklist and adhering to non-negotiable standards.

Do I need data analysis skills to evaluate AI features?▼

Yes, evaluating AI features requires human validation and data analysis skills. The framework provides expert references and mental models, but users must analyze data to apply the evaluation pyramid effectively.

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