Data-Driven RAG Evaluation: Testing Qdrant Apps with Relari AI

Qdrant and Relari have teamed up to streamline the evaluation of Retrieval-Augmented Generation (RAG) systems by providing an efficient framework that leverages both intrinsic and extrinsic evaluation methods. This partnership allows developers to conduct fast, iterative testing using Qdrant's vector database for data storage and retrieval, and Relari's tools for running experiments to evaluate performance in real-world scenarios. Key strategies include Top-K Parameter Optimization, which adjusts the number of top results shown to users to enhance the user experience, and Auto Prompt Optimization (APO), which refines chatbot interactions to improve communication effectiveness. The process includes using synthetic and real datasets, such as the GitLab legal policies, to establish benchmarks for testing and analyzing different configurations. By integrating these methods, developers can enhance the accuracy and user satisfaction of RAG systems, making them more responsive to user needs and improving their overall performance.

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