RAG pipelines are everywhere—but most are barely holding together. As GenAI moves from demos to production, the cracks are showing: silent failures, hallucinations, and a total lack of insight into what your AI is actually doing.

This talk introduces a new architectural mindset for building RAG systems that scale reliably and responsibly—with traceability and control built in from day one.

You’ll learn how to:

- Log retrievals, prompts, and responses in a way that exposes true decision lineage
- Trace outputs back to the documents—and even chunks—that influenced them
- Identify high-value patterns, failure clusters, and prompt blind spots over time

Use graph-native tools like Neo4j to map and monitor the system as it evolves

Whether you're running a few thousand queries or scaling to millions, this approach turns RAG into a system you can debug, explain, and trust. Because if AI is going to power real applications, we need more than answers—we need architecture.