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.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# 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
**Classification:**
- **Accuracy**: Percentage correct
- **Precision/Recall/F1**: Class-specific performance
- **Confusion Matrix**: Error patterns
- **AUC-ROC**: Ranking quality
**Retrieval (RAG):**
- **MRR**: Mean Reciprocal Rank
- **NDCG**: Normalized Discounted Cumulative Gain
- **Precision@K**: Relevant in top K
…
The line at the top of this page pins the label git:20251120.50bf83d, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
GET https://markdownregistry.com/api/v1/artifacts/art_r5hi5xrfbqb4bp4q
GET https://markdownregistry.com/api/v1/resolve?ref=hermeticormus/claude-code-game-development/llm-evaluation
GET https://markdownregistry.com/api/v1/blob/b37039b062cd5d977ab66863cd74b493686a5ec2d25b04178a0032df4c6c2b59
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
hermeticormus/claude-code-game-development · .grok/skills/verify-claude-code-game-development/SKILL.md · Install the claude-code-game-development plugin pack the way a user does, into a clean Claude Code config and a clean…
hermeticormus/claude-code-game-development · plugins/audio-systems/skills/audio-patterns/SKILL.md · Game audio code patterns: a Godot bus layout, an audio player pool, randomized one-shot playback, vertical-remix…
hermeticormus/claude-code-game-development · plugins/backend-development/skills/api-design-principles/SKILL.md · Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight…
hermeticormus/claude-code-game-development · plugins/backend-development/skills/temporal-python-testing/SKILL.md · Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing…
hermeticormus/claude-code-game-development · plugins/blockchain-web3/skills/defi-protocol-templates/SKILL.md · Implement DeFi protocols with production-ready templates for staking, AMMs, governance, and lending systems. Use when…
martineserios/thebrana · system/skills/acquired/llm-evaluation/SKILL.md · LLM evaluation — automated metrics, human feedback, benchmarking. Use when testing performance, measuring AI quality…
