Best Practices for Production-Scale RAG Systems — An Implementation Guide

The implementation of Retrieval-Augmented Generation (RAG) systems enhances AI model responses by integrating background knowledge from databases, useful for tasks such as financial analysis or policy advising. This process involves chunking and storing information, which is retrieved based on user queries to improve AI-generated responses. However, challenges arise in maintaining context and retrieval precision, often due to the lossy nature of vector embeddings. Best practices to mitigate these issues include reintroducing context through document headers or summaries, using semantic chunking to preserve meaning, and employing hybrid search techniques combining keyword and vector search methods. Reranking retrieved information further refines search results. An orchestration platform like Orkes Conductor can facilitate building and monitoring RAG systems by managing workflows across distributed components, enabling the integration of various search and indexing strategies. Conductor allows for flexible and resilient system design, providing visibility and management of workflow processes, which is crucial for optimizing AI interactions and ensuring reliable execution in complex systems.