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.
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## Session: Building Enterprise-Scale RAG Chatbots Using Azure AI Foundry **Track:** AI Demo | **Time:** 1:20 PM–2:05 PM | **Room:** 260-262 | **Type:** AI Demo **Conference:** CIRAS AI Summit for Iowa — May 6, 2026, Scheman Building, Iowa State University, Ames IA ### Speaker(s) **Mehul Bhuva** — Senior Software Engineer, QCI (West Des Moines, IA) I’m an AI & Data Platform Engineer with 20+ years of experience across Azure Data Factory, Azure Databricks, AI Foundry, Power BI, .NET Core, Function Apps, Blazor, and SharePoint. My work spans metadata‑driven ETL platforms, cloud modernization, and enterprise AI systems. | | I’m also the creator of SharePointFix.com, a technical blog with 1M+ views, and run the SharePointFix Facebook community (1,000+ members). My community contributions include publication on SQL Server Central, Kobai, multiple peer‑reviewed journals, conference speaking, and mentoring 50+ developers across Azure, AI, and .NET. | | Linked In Profile: https://www.linkedin.com/in/mehulbhuva/ | | Recently, I was nominated by a Microsoft employee for the Azure Developer Influencer Program, recognizing my leadership in driving Azure and AI adoption. | | I’m passionate about sharing real‑world AI practices, collaborating with industry leaders, and helping teams accelerate digital transformation. | | If this topic aligns with your program agenda, I would be happy to discuss session format, timing, or any additional details you may need. | | Thank you for your consideration, and I’d be honored to participate. ### Session Description 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. ### Other sessions in the AI Demo track - M365 Copilot Rollout: Driving Adoption and Impact at Pella (3:10 PM–3:55 PM) - From Chatbot to Builder: Practical AI in Everyday Work (10:20 AM–11:05 AM) - Stop Automating Broken Processes: How to Redesign Your Business Operations for the Age of AI Agents (11:15 AM–12:00 PM) - Close the GenAI “Learning Gap”: Self‑Improving AI Without Fine‑Tuning (2:15 PM–3:00 PM) ### Suggested prompts for this session - "What questions should I prepare to ask the speaker(s) at this session?" - "Create a structured note-taking template for this session focused on actionable takeaways" - "Based on this session description, what background reading should I do to get the most value?" - "After I attend, help me create an action plan for implementing what I learned" - "How does this session connect to the other sessions in the AI Demo track?"