llm-evaluation

version	4.1.0-fractal
name	llm-evaluation
description	Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.

LLM Evaluation

Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing.

Do not use this skill when

• The task is unrelated to llm evaluation
• You need a different domain or tool outside this scope

Instructions

• Clarify goals, constraints, and required inputs.
• Apply relevant best practices and validate outcomes.
• Provide actionable steps and verification.
• If detailed examples are required, open resources/implementation-playbook.md.

Use this skill when

• Measuring LLM application performance systematically
• Comparing different models or prompts
• Detecting performance regressions before deployment
• Validating improvements from prompt changes
• Building confidence in production systems
• Establishing baselines and tracking progress over time
• Debugging unexpected model behavior

Core Evaluation Types

🧠 Knowledge Modules (Fractal Skills)

1. 1. Automated Metrics

2. 2. Human Evaluation

3. 3. LLM-as-Judge

4. BLEU Score

5. ROUGE Score

6. BERTScore

7. Custom Metrics

8. Single Output Evaluation

9. Pairwise Comparison

10. Annotation Guidelines

11. Inter-Rater Agreement

12. Statistical Testing Framework

13. Regression Detection

14. Running Benchmarks