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The RAG reality check: New open-source framework lets enterprises scientifically measure AI performance
New open-source evaluation framework quantifies RAG pipeline performance with scientific metrics, helping enterprises cut through the AI hype cycle with objective measurements.
“In information retrieval and dense vectors, you could measure lots of things, ndcg [Normalized Discounted Cumulative Gain], precision, recall…but when it came to right answers, we had no way, that’s why we started on this path.” Importantly, the framework evaluates the entire RAG pipeline end-to-end, providing visibility into how embedding models, retrieval systems, chunking strategies, and LLMs interact to produce final outputs. For enterprises looking to lead in AI adoption, Open RAG Eval means they can implement a scientific approach to evaluation rather than relying on subjective assessments or vendor claims.
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