Retrieval-Augmented Generation is everywhere in demos — but production enterprise deployments look very different. In this session, I'll walk through the end-to-end architecture of a real RAG system built on Azure, drawing from my experience taking a GenAI platform from zero to 10 enterprise customers at a YCombinator-backed startup.
We'll cover: how Azure AI Search handles vector indexing and hybrid retrieval, how to wire it to Azure OpenAI for grounded, context-aware responses, and the practical decisions that separate a working prototype from something you'd trust with enterprise data. Topics include chunking strategies, embedding pipelines, retrieval quality tuning, and where the system breaks under production load.
Attendees will leave with a clear mental model of the Azure services involved (Azure AI Search, Azure OpenAI, Azure Blob + data pipelines), a realistic picture of what goes wrong and how to debug it, and a starting architecture they can adapt to their own use case. No prior RAG experience needed — intermediate Azure familiarity is sufficient.