Building Enterprise-Scale RAG Chatbots Using Azure AI Foundry

This session provides a practical, end‑to‑end deep dive into building enterprise‑grade Retrieval Augmented Generation (RAG) systems using Microsoft Azure AI Foundry. Drawing from real‑world implementations—including a production RAG chatbot serving 22,000+ global users—the session walks participants through the complete lifecycle of creating scalable, secure, and high‑accuracy RAG solutions tailored for industry.

We begin by breaking down the RAG architecture: document ingestion using Azure Document Intelligence, adaptive chunking strategies, embedding generation, and vector indexing with Azure AI Search. The session then explores how user queries are transformed into embeddings, how retrieval pipelines work, and how Azure OpenAI models inside AI Foundry generate grounded, contextual responses.

Using visuals from the included architecture diagram, attendees learn best practices for chunk sizes, metadata, hybrid search, reranking, prompt design, and governance (RBAC, encryption, audit logging). Real‑world examples from agriculture, manufacturing, financial services, and healthcare show how organizations use RAG for maintenance assistants, compliance bots, crop advisory tools, customer service, workflow documentation, and more. Performance benchmarks—such as 95% retrieval accuracy, sub‑2‑second responses, and 40%–60% cost reduction—demonstrate measurable business impact.

The session concludes with an interactive discussion on emerging trends—multi‑modal RAG, knowledge graphs, and real‑time streaming—and how Azure AI Foundry’s roadmap supports the next generation of enterprise AI.

This is a highly actionable, architecture‑focused session designed for leaders, engineers, and practitioners looking to implement RAG at scale with Microsoft technologies.

• Intermediate
• Development
• AI/ML