RAG at Scale - Logging, Traceability, and the Architecture for Control

Explore a comprehensive conference talk that addresses the critical challenges of scaling Retrieval-Augmented Generation (RAG) systems in production environments. Learn how to transform fragile RAG pipelines into robust, traceable architectures that can handle enterprise-scale demands while maintaining reliability and transparency. Discover essential techniques for implementing comprehensive logging systems that capture retrievals, prompts, and responses to create clear decision lineage throughout your AI pipeline. Master the art of tracing AI outputs back to their source documents and specific chunks, enabling you to understand exactly how your system arrives at its conclusions. Gain insights into identifying valuable patterns, failure clusters, and prompt blind spots through systematic monitoring and analysis over time. Understand how to leverage graph-native tools like Neo4j to map, monitor, and evolve your RAG architecture as it scales from thousands to millions of queries. Address the fundamental shift from demonstration-ready prototypes to production-grade systems that can be debugged, explained, and trusted in real-world applications. Learn architectural principles that prioritize traceability and control from the initial design phase, preventing common issues like silent failures and hallucinations that plague many RAG implementations. Whether you're dealing with current scaling challenges or preparing for future growth, acquire the knowledge needed to build RAG systems that deliver not just answers, but accountability and architectural integrity.