Retrieval Augmented Generation (RAG)

Join hosts Lois Houston and Nikita Abraham as they explore one of the most exciting innovations in enterprise AI: Retrieval Augmented Generation (RAG) powered by Oracle AI Vector Search. In this episode, Senior Principal APEX & Apps Dev Instructor Brent Dayley walks through the fundamentals of RAG, explaining how it combines Oracle Database 23ai, vector embeddings, and large language models to deliver accurate, context-rich answers from both business and unstructured data. Discover the typical RAG workflow, practical setup steps on Oracle Cloud Infrastructure, and how to work with embedding models for real-world applications. Oracle AI Vector Search Deep Dive: https://mylearn.oracle.com/ou/course/oracle-ai-vector-search-deep-dive/144706/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu Special thanks to Arijit Ghosh, Anna Hulkower, Kris-Ann Nansen, and the OU Studio Team for helping us create this episode. Please note, this episode was recorded before Oracle AI Database 26ai replaced Oracle Database 23ai. However, all concepts and features discussed remain fully relevant to the latest release. ---------------------------------------------- Episode Transcript 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:26 Nikita: Welcome to the Oracle University Podcast! I'm Nikita Abraham, Team Lead: Editorial Services with Oracle University, and joining me is Lois Houston, Director of Communications and Adoption Programs with Customer Success Services. Lois: Hi everyone! If you've been with us this season, you'll know we've already covered a lot about Oracle AI Vector Search. In Episode 1, we introduced the core concepts—how vectors let you search by meaning, not just keywords, and how embedding models translate your unstructured data into a searchable format inside Oracle Database 23ai. Nikita: Then, in Episode 2, we took a deeper dive into how these vectors are actually stored and managed. We explored the different types of vector indexes, similarity metrics, and best practices for designing and optimizing your database for semantic search. Lois: Right. Today, we're shifting gears into one of the most exciting real-world applications: Retrieval Augmented Generation, or RAG. You'll learn how RAG combines the power of Oracle AI Vector Search with large language models to answer natural language questions using both business and unstructured data. 01:39 Nikita: We'll walk through the workflow, highlight why Oracle Database is uniquely suited for RAG, and give you the essential steps to get started. Back again is Senior Principal APEX & Apps Dev Instructor Brent Dayley. Hi Brent! Could you explain what RAG is, and why it's important for working with AI and large language models? Brent: Well, RAG stands for Retrieval Augmented Generation. And this is a technique that allows us to enhance the capabilities of large language models, also known as LLMs, and this provides them with relevant context from external knowledge sources. This will allow the LLMs to generate more accurate, informative, and context-aware responses. Real world applications include answering questions, chatbot development, content summarization, and knowledge discovery. 02:35 Lois: Brent, what makes Oracle Database 23ai a good platform for implementing RAG workflows? Brent: Now, there are some key advantages of using Oracle Database 23ai as a RAG platform. These include native functionality, allowing built-in tools and packages specifically designed for RAG pipeline development. Also, if you are a PL/SQL developer, then this will allow you to develop within a familiar and robust database environment. Also, Oracle has a plethora of security and performance tools. And this ensures enhanced security and optimized performance. 03:18 Nikita: What does a typical RAG workflow look like in Oracle Database 23ai? What are the main steps involved? Brent: Now, the primary workflow steps are going to be to generate vector embeddings from your unstructured data. You do this using vector embedding models. And you can generate those embeddings either inside or outside of the database. Next, you need to store the vector embeddings, the unstructured data, and the relational business data, and you can store all of that in the Oracle Database. You might want to also create vector indexes that can allow you to run similarity searches over huge vector spaces with really good performance. Finally, you need to query data with similarity searches. You can use Oracle AI Vector Search native SQL operations to combine similarity with relational searches to retrieve relevant data. And optionally, you can generate a prompt and send it to a ...