Build a RAG App in 10 Minutes (No Code) | Azure AI Search + Agent Foundry

Description

In this video, we build a complete RAG (Retrieval-Augmented Generation) application without writing a single line of code using Azure AI Search, Agent Foundry, and GPT-4.1 Mini. 📦 Resources & Links: LLM with RAG vs LLM Without RAG: https://youtu.be/WMIOCFK8N24 Agentic RAG-Part-1: https://youtu.be/MDbKg9q_UJk Agentic RAG-Part-2: https://youtu.be/LhlT5xwt-pQ Chat GPT LLM Finetuning: https://youtu.be/y3_0d-_rAgY MAF End to end Project: https://youtu.be/BAdcNKf5d2g Introduction to Microsoft Agent Framework: https://youtu.be/Gxh6fef4jJU LangGraph Tutorial: https://www.youtube.com/playlist?list=PLLPrkPALrjwtrIwOgtEq9cBPMF3Svae_G Agent Foundry Mastery: https://youtu.be/-gaFEfFJYLc?si=Kp9z8m2ISz6fo6vc Autogen Playlist: https://www.youtube.com/playlist?list=PLLPrkPALrjwthstGWapGz9NRuy6p8cVTX If you’ve ever wondered how real-world AI applications use company data to give accurate answers instead of hallucinating, this video walks you through the entire process step by step. We start by uploading company data (policies and FAQs) into Blob Storage and then use Azure AI Search to create a knowledge base. The data is processed using the text-embedding-small model to generate embeddings, indexed efficiently, and made ready for retrieval. Next, we connect Azure AI Search with Agent Foundry, where we configure the agent to use GPT-4.1 Mini for response generation. The agent uses hybrid search (keyword + vector search) along with semantic ranking to retrieve the most relevant information. Finally, we test everything in the Chat Playground and see how the system provides accurate, context-aware answers based on our data. 🔍 What you’ll learn in this video: What is RAG and why it is important How to build a no-code RAG pipeline using Azure services How embeddings work using text-embedding-small model How indexing works in Azure AI Search What is hybrid search and semantic search How Agent Foundry connects with Azure AI Search How GPT-4.1 Mini generates accurate responses using retrieved data ⚙️ Architecture Covered: Blob Storage (data source) Embedding Model (text-embedding-small) Azure AI Search (indexing + retrieval) Hybrid + Semantic Search Agent Foundry (RAG orchestration) GPT-4.1 Mini (LLM for response generation) Chat Playground (testing interface) This video is perfect for developers, data scientists, and anyone looking to build real-world Generative AI applications using Azure. 🔥 Keywords: RAG tutorial, Azure AI Search tutorial, Agent Foundry tutorial, GPT-4.1 Mini, vector search, semantic search, embeddings explained, no code AI app 📌 Hashtags #RAG #AzureAISearch #GenerativeAI #LLM #GPT4 #AIProjects #MachineLearning #VectorSearch #SemanticSearch #AIForDevelopers #NoCodeAI #AgentFoundry #OpenAI #AIArchitecture #AIApps