Retrieval Augmented Generation (RAG) is a popular technique to get LLMs to answer questions based off your own knowledge base.

"Agentic RAG" adds planning, tool use, and reflection to a RAG flow. In this session, we'll demonstrate an Agentic RAG workflow using OpenAI Function Calling with NL2SQL for structured data, Azure AI Search for unstructured data, and Bing Search API for live web search.

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Follow along: - Create agentic AI solutions by using Azure AI Foundry - Practical Foundation for AI Agents: A Developer's Guide on Azure AI Foundry, Apps, and Data

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Pre-requisites: * Join the Hackathon * Learning Resources