Episode
Retrieval Augmented Generation (RAG) and Vector Databases (Part 15 of 18) | Generative AI for Beginners
with Bethany Jepchumba
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.
Recommended resources
- The full "Generative AI for Beginners" Course
- After completing this lesson, check out our Generative AI Learning collection to continue leveling up your Generative AI knowledge!
- Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service
- Perform vector search and retrieval in Azure AI Search
Related episodes
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.
Recommended resources
- The full "Generative AI for Beginners" Course
- After completing this lesson, check out our Generative AI Learning collection to continue leveling up your Generative AI knowledge!
- Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service
- Perform vector search and retrieval in Azure AI Search
Related episodes
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