RAG Metrics
Overview of RAG metrics in the RagaAI Metric Library. Measure hallucination, faithfulness, and context use to assess LLM accuracy and reliability with external knowledge.
Last updated
Was this helpful?
Overview of RAG metrics in the RagaAI Metric Library. Measure hallucination, faithfulness, and context use to assess LLM accuracy and reliability with external knowledge.
RAG (Retrieval-Augmented Generation) Metrics help you measure how well your retrieval pipelines and generation layers are performing inside RagaAI Catalyst. Since RAG systems combine search + LLM reasoning, monitoring both sides is critical to ensure reliability, accuracy, and efficiency.
Detect gaps in retrieval: Spot when your retriever fails to surface the most relevant passages.
Evaluate generated answers: Check if the model’s outputs are grounded in retrieved context.
Compare retrievers and models: Benchmark different embeddings, vector stores, or LLMs with the same dataset.
Optimize cost vs quality: Find the balance between wider retrieval vs faster responses.
Last updated
Was this helpful?
Was this helpful?