evaluating-llms

What problem does it solve?

This Skill provides a comprehensive framework for evaluating LLM performance, ensuring your AI applications are accurate, reliable, and safe.

Core Features & Use Cases

• Automated Metrics: Utilize metrics like BLEU, ROUGE, and BERTScore for generation tasks.
• LLM-as-Judge: Employ powerful LLMs to assess nuanced quality criteria with custom rubrics.
• RAG Evaluation: Measure faithfulness, relevance, and context quality using the RAGAS framework.
• Safety Testing: Detect hallucinations, bias, and toxicity in LLM outputs.
• Benchmark Testing: Assess models against standards like MMLU and HumanEval.
• Use Case: You've built a RAG system and need to ensure its answers are factual and relevant. Use this Skill's faithfulness and relevance metrics to validate its performance before deployment.

Quick Start

Use the evaluating-llms skill to run a RAGAS faithfulness check on your system's output.