Optimizing RAG Systems with LlamaIndex: Strategies for Production Performance

Prototyping a Retrieval-Augmented Generation (RAG) application is relatively straightforward, but the challenge lies in optimizing it for performance, robustness, and scalability across vast knowledge repositories. This guide aims to provide insights, strategies, and implementations leveraging LlamaIndex to enhance the efficiency of your RAG pipeline, catering to complex datasets and ensuring accurate query responses without hallucinations.

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