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Retrieval Augmented Generation (RAG) enables LLMs to answer questions related to specific data sets that were not part of the LLM’s original training data, like business specific proprietary data or data related to real-time events. This approach enables enterprises to leverage the natural language capabilities while ensuring that the information provided to the user to grounded in proprietary enterprise data. While this approach is promising, there are a set of checks and balances that need to be performed before productionizing RAGs to ensure the authenticity and usefulness of the data provided to the user. In this article we go over a popular RAG evaluation named RAGAS and explain how we utilize RAGAS to ensure that the RAGs developed on our platform meet the highest quality requirements.

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