ai-evaluation-framework

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.