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.

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.

Why RAG Metrics matter

• 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.