llm-evals-and-retrieval-quality
CommunityMaster LLM evaluation and retrieval quality metrics with precision.
Authorjpoindexter
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill provides a comprehensive guide to evaluating and improving the quality of LLM and RAG systems, ensuring robust performance and accurate retrieval.
Core Features & Use Cases
- LLM Evaluation: Offers detailed guidance on various evaluation sets, including golden, regression, adversarial, unit, and end-to-end evaluations.
- Retrieval Metrics: Delivers insights into retrieval metrics like recall@k, MRR, nDCG, and context-level RAG metrics.
- Grounding and Faithfulness: Focuses on grounding, faithfulness, and attribution to ensure the accuracy of generated content.
- Eval Set Construction: Provides guidance on building and maintaining high-quality eval sets for continuous improvement.
- Process Integration: Integrates evaluation processes into development workflows for effective quality control.
Quick Start
Load the llm-evals-and-retrieval-quality skill and follow the comprehensive guide to evaluate your LLM and RAG systems.
Dependency Matrix
Required Modules
None requiredComponents
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: llm-evals-and-retrieval-quality Download link: https://github.com/jpoindexter/design-and-ai-skills/archive/main.zip#llm-evals-and-retrieval-quality Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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