Building Performant RAG Applications for Production - LlamaIndex 🦙 0.9.23

Prototyping a RAG application is easy, but making it performant, robust, and scalable to a large knowledge corpus is hard. This guide contains a variety of tips and tricks to improve the performance of your RAG pipeline. We first outline some general techniques - they are loosely ordered in terms of most straightforward to most challenging. We then dive a bit more deeply into each technique, the use cases that it solves, and how to implement it with LlamaIndex! The end goal is to optimize your retrieval and generation performance to answer more queries over more complex datasets accurately and without hallucinations. Here are some top Considerations for Building Production-Grade RAG Decoupling chunks used for retrieval vs. chunks used for synthesis Structured Retrieval for Larger Document Sets Dynamically Retrieve Chunks Depending on your Task Optimize context embeddings We discussed this and more during our Production RAG Webinar. Check out this Tweet thread for more synthesized detai

Building Performant RAG Applications for Production | Developer Documentation Skip to content Building Performant RAG Applications for Production Prototyping a RAG application is easy, but making it performant, robust, and scalable to a large knowledge corpus is hard. This guide contains a variety of tips and tricks to improve the performance of your RAG workflow. We first outline some general techniques - they are loosely ordered in terms of most straightforward to most challenging. We then dive a bit more deeply into each technique, the use cases that it solves, and how to implement it with

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