Avoiding Data Leakage in Synthetic Data Projects
Synthetic data offers vast opportunities for machine learning model training without real-world constraints. Yet, beneath this promise lies the risk of…
Read more →Ragas is an open-source framework, created by Shahul ES and Jithin James under the ExplodingGradients project, that provides automated metrics for evaluating retrieval-augmented generation (RAG) systems, including faithfulness, answer relevancy, context precision, and context recall. It also generates synthetic evaluation datasets and supports production monitoring, and is integrated by LangChain’s LangSmith and LlamaIndex.
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