Transitioning Retrieval-Augmented Generation (RAG) systems from experimental setups to live production environments requires careful planning and sound engineering practices. This initial chapter lays the groundwork for building RAG systems that are not only functional but also performant, scalable, and maintainable when faced with real-world operational demands.
We will examine the architectural components that are essential to scaling RAG systems effectively. You will learn to pinpoint common performance bottlenecks that can affect responsiveness and efficiency. The discussion will then move to advanced metrics tailored for evaluating RAG systems in production, which offer more specific insights than basic accuracy measures. This chapter also prepares you for the ongoing challenges of maintaining RAG systems over time. This includes making necessary infrastructure choices, understanding the importance of version control for models and data, implementing effective experiment tracking, and establishing fundamental security measures to protect your deployments.
A grasp of these foundational aspects will support your understanding of the advanced optimization techniques detailed in subsequent chapters.
1.1 Production RAG Architecture: Scaling Considerations
1.2 Identifying Performance Bottlenecks in RAG Pipelines
1.3 Advanced Metrics for Production RAG Evaluation
1.4 Long-Term Maintenance Challenges for RAG Systems
1.5 Production Infrastructure Considerations for RAG
1.6 Version Control and Experiment Tracking for RAG Components
1.7 Security Considerations in Production RAG
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