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Building RAG Solutions with Azure AI Studio

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This in-depth course is crafted to empower you with the proficiency to effectively leverage Azure AI Studio for the creation of retrieval-augmented generation (RAG) solutions. The course begins with a systematic walkthrough of the process involved in creating a RAG solution and the fundamentals and principles of RAG, diving into the prerequisites for crafting a RAG solution in Azure AI Studio. This hands-on course with instructor Ziggy Zulueta offers demonstrations on importing and vectorizing data, and integrating this data into a deployed model, showing the process of customizing the default solution using the prompt flow feature. The course wraps up with a guide on testing and evaluating the solution, along with a practical session on testing the REST API endpoints in Postman and using the endpoint in Microsoft Teams through Copilot Studio.


01 - Introduction
  • 01 - Create a RAG solution with little coding

  • 02 - 1. Fundamentals of RAG
  • 01 - The basics of RAG Adding custom data to your LLM
  • 02 - Understanding tokens A key factor of costs in your system
  • 03 - Vector embeddings How words connect to each other
  • 04 - How RAG works Understanding the process under the hood
  • 05 - RAG high-level architecture The required components

  • 03 - 2. Introduction to Azure AI Studio
  • 01 - Azure AI Studio overview Deploy at scale in a safe, secure, and responsible way
  • 02 - Navigating the Azure AI Studio
  • 03 - Creating a project in Azure AI Studio

  • 04 - 3. Setting Up Azure AI Studio
  • 01 - Understanding content filters
  • 02 - Creating content filters
  • 03 - Creating model deployments
  • 04 - Navigating the Playground
  • 05 - Using the Playground and its settings

  • 05 - 4. Creating an Index for RAG Using Azure
  • 01 - Creating an index using Azure AI Studio
  • 02 - Creating an index using Azure AI Search
  • 03 - Understanding retrieval and relevance in Azure AI Search
  • 04 - Testing your index in the Playground

  • 06 - 5. Introduction to Azure Prompt Flow
  • 01 - Understanding prompt flow
  • 02 - Create a sample prompt flow for RAG
  • 03 - Evaluation and monitoring metrics
  • 04 - Perform evaluations on your RAG system

  • 07 - 6. Deploying a RAG Solution
  • 01 - Deploying the RAG solution using prompt flow
  • 02 - Testing the REST endpoint using Postman
  • 03 - Deploying the REST endpoint to Copilot Studio and Microsoft Teams

  • 08 - Conclusion
  • 01 - Key takeaways
  • 02 - Additional learning
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