Authors and runs Ragas - RAG-pipeline evaluation framework with metrics organized into RAG (Faithfulness, Response Relevancy, Context Precision/Recall, Context Entities Recall, Noise Sensitivity), Natural Language Comparison (Factual Correctness, Semantic Similarity, BLEU/ROUGE/CHRF/Exact Match), Agents/Tool-Use (Topic Adherence, Tool Call Accuracy/F1, Agent Goal Accuracy), General Purpose (Aspect Critic, Rubrics-based Scoring), Nvidia (Answer Accuracy, Context Relevance, Response Groundedness), and Summarization. Use when the user evaluates a RAG pipeline (retriever + generator) and needs the deepest metric variety in the OSS LLM-eval space.
80
100%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
View guide
Low
Low-risk findings worth noting
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"description": "Authors and runs Ragas - RAG-pipeline evaluation framework with metrics organized into RAG (Faithfulness, Response Relevancy, Context Precision/Recall, Context Entities Recall, Noise Sensitivity), Natural Language Comparison (Factual Correctness, Semantic Similarity, BLEU/ROUGE/CHRF/Exact Match), Agents/Tool-Use (Topic Adherence, Tool Call Accuracy/F1, Agent Goal Accuracy), General Purpose (Aspect Critic, Rubrics-based Scoring), Nvidia (Answer Accuracy, Context Relevance, Response Groundedness), and Summarization. Use when the user evaluates a RAG pipeline (retriever + generator) and needs the deepest metric variety in the OSS LLM-eval space.",
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