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.
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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.
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Stars38,171
Forks4,092
LanguagePython
LicenseMIT
📦 Ships with wshobson-agents
</> SKILL.md
llm-evaluation.SKILL.md
---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.
## When to Use This Skill
- 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
### 1. Automated Metrics
Fast, repeatable, scalable evaluation using computed scores.
**Text Generation:**
- **BLEU**: N-gram overlap (translation)
- **ROUGE**: Recall-oriented (summarization)
- **METEOR**: Semantic similarity
- **BERTScore**: Embedding-based similarity
- **Perplexity**: Language model confidence
