Episode

Microsoft Learn AI Skills Challenge Ep08: Developing a production level RAG workflow

with Graeme Malcolm, Carlotta Castelluccio, Hamid Sadeghpour Saleh

Learn Live

Copilots can work alongside you to provide suggestions, generate content, or help you make decisions. Copilots use language models as a form of generative artificial intelligence (AI) and will answer your questions using the data they were trained on. To ensure a copilot retrieves information from a specific source, you can add your own data when building a copilot with the Azure AI Studio (preview).

Learning objectives

  • Identify the need to ground your language model with Retrieval Augmented Generation (RAG)
  • Index your data with Azure AI Search to make it searchable for language models
  • Build a copilot using RAG on your own data in the Azure AI Studio

Chapters

  • 00:00 - Introduction
  • 05:35 - Learning objectives
  • 06:35 - What is a copilot?
  • 11:47 - Copilot Development Lifecycle
  • 14:48 - Building a copilot on your data
  • 17:49 - Incorporating domain knowledge
  • 21:25 - Retrieval Augmented Generation (RAG)
  • 28:22 - Demo - Adding data and indexes in Azure AI Studio
  • 38:51 - Q&A
  • 43:24 - Prompt flow
  • 50:01 - Demo - Using a RAG pattern in a prompt flow
  • 01:07:10 - Knowledge check
  • 01:15:12 - Summary

Connect

Intermediate
Data Scientist
AI Engineer
Azure
Azure AI services