# Open-source RAG evaluation Get the metrics and monitoring you need to rigorously evaluate and up-level the performance of your RAG systems. [Access the GitHub repo](https://github.com/TonicAI/tonic_validate) .svg)[Text Link](https://pypi.org/project/tonic-validate/) Pip install tonic-validate ## Measure and confirm performance Assess accuracy, context quality, latency, and more. Tonic Validate has custom-built metrics that isolate and test each component of your RAG application. ## Track, compare, and iterate on experiments Track RAG parameters and responses by logging them to Tonic Validate. Visualize how metrics change across experiments to perfect your RAG system. ## Monitor your RAG system in production Use Tonic Validate’s logger to collect real user queries, context-augmented prompts, and LLM responses to monitor your RAG system’s performance in production. "I'm impressed with Tonic Validate thus far. I love the Web UI they provide for monitoring and observability. Simply adding a few lines of code to connect to a LlamaIndex vector store and query engine for running evaluations at scale is an AI Engineer's dream come true!" Farzad Sunavala Senior Product Manager .webp) ### Partners [LlamaIndex](https://www.llamaindex.ai/) is a "data framework" to help build LLM apps. It provides data connectors to ingest existing data sources and formats and enables data to be structured and easily used with LLMs. You can leverage the advanced evaluation metrics from Tonic Validate directly within LlamaIndex's platform and visualize experiments and monitor RAG performance easily using our UI. ## Strengthen your RAG applications with Tonic Validate today Use Tonic Validate to rigorously evaluate and improve your RAG applications. [Get started](https://github.com/TonicAI/tonic_validate)