> For the complete documentation index, see [llms.txt](https://docs.raga.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/rag-metrics.md).

# 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.