Build a RAG Chatbot with Azure OpenAI and Azure AI Search in Python

Building a RAG chatbot is easy to demo and hard to make accurate — this walks the full Python build where retrieval quality actually comes from. You'll wire up Azure OpenAI embeddings plus Azure AI Search as a vector store, with a concrete ingestion pipeline (500–1000 token chunks, 10–15% overlap) and a query flow that grounds answers and returns citations. The non-obvious parts: your index vector field dimensions must match the embedding model exactly (3072 for text-embedding-3-large), you must embed questions with the same model used at ingestion, and hybrid search plus a strict grounding prompt is what separates real answers from plausible hallucinations. For engineers deciding between the managed "on your data" integration and a custom pipeline they can tune and debug in production. ⏱️ Chapters: 0:00 Intro 0:04 What We're Building 0:41 The Architecture 1:29 Ingestion Pipeline 2:14 Defining the Search Index 2:55 The Query Flow 3:40 A Build vs Buy Decision 4:18 Verify It Works 4:59 Recap and Takeaway Subscribe for more end-to-end Azure builds you can rebuild from memory. Check the current Azure docs — cloud services change. #AzureOpenAI #AzureAISearch #RAG #VectorSearch #Python