Industry-standard metrics for evaluating Retrieval-Augmented Generation systems, including Answer Relevancy, Faithfulness, Context Relevance, Context Recall, and Context Precision to ensure quality and reliability.
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Context Precision
RAG evaluation metric assessing retriever's ability to rank relevant chunks higher than irrelevant ones, measuring context relevance and ranking quality for optimal retrieval.
RAGAS
Retrieval Augmented Generation Assessment framework for reference-free evaluation of RAG pipelines. RAGAS provides automated metrics for retrieval quality, context relevance, and generation faithfulness.
Retrieval Metrics
Performance measurement framework for vector search and RAG systems including recall, precision, nDCG, MRR, and context relevance metrics to evaluate retrieval quality and relevance.
Context Recall
RAG evaluation metric measuring whether retrieved context contains all information required to produce ideal output, assessing completeness and sufficiency of retrieval.
Faithfulness
RAG evaluation metric measuring whether generated answers accurately align with retrieved context without hallucination, ensuring factual grounding of LLM responses.
LLM-as-Judge Evaluation
Using language models to automatically evaluate RAG system outputs, retrieval quality, and answer correctness. LLM-as-judge provides scalable, consistent evaluation of aspects like faithfulness, relevance, and coherence that are difficult to measure with traditional metrics, enabling rapid iteration on RAG systems.
