Most teams building with LLMs can tell you if their system is up or down. Very few can tell you why it gave a wrong answer last Tuesday, or which retrieval step failed silently. This session walks through the end-to-end architecture of a production RAG system on Azure, drawing from experience taking a GenAI platform from zero to 10 enterprise customers.
We'll cover how Azure AI Search handles vector indexing and hybrid retrieval, how to wire it to Azure OpenAI for grounded 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 systems break under production load.