Retrieval Augmented Generation (RAG) and Vector Databases [Pt 15] | Generative AI for Beginners

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Find the full "Generative AI for Beginners" Course here ➡️ https://aka.ms/genai-beginners In the search applications lesson, we briefly learned how to integrate your own data into Large Language Models (LLMs). In this lesson, we will delve further into the concepts of grounding your data in your LLM application, the mechanics of the process and the methods for storing data, including both embeddings and text. 🌟 In this lesson we will cover the following: ✅ An introduction to RAG, what it is and why it is used in AI (artificial intelligence). ✅ Understanding what vector databases are and creating one for our application. ✅ A practical example on how to integrate RAG into an application. Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service: https://learn.microsoft.com/en-us/training/modules/use-own-data-azure-openai/?WT.mc_id=academic-105485-koreyst Perform vector search and retrieval in Azure AI Search: https://learn.microsoft.com/en-us/training/modules/improve-search-results-vector-search/?WT.mc_id=academic-105485-koreyst&ns-enrollment-type=Collection&ns-enrollment-id=zpy7c8zmq6ky0z%3FWT.mc_id%3Dacademic-105485-koreyst 🧠 After completing this lesson, check out our Generative AI Learning collection - https://aka.ms/genai-collection to continue leveling up your Generative AI knowledge!

Published 2024-06-25 · Watch on YouTube ↗
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