Learning Objectives
In this course, you will learn how to implement Retrieval Augmented Generation (RAG) using KDB.AI and OpenAI.
You’ll gain hands-on experience in setting up a RAG pipeline, including data preparation, embedding generation, vector database operations, and integration with large language models.
Prerequisites
This course is aimed at developers and data scientists who have basic knowledge of Python programming and/or are familiar with concepts of machine learning and natural language processing.
Prior experience with vector databases or large language models is helpful but not required.
Note: This course includes practical coding examples and demonstrations. It’s recommended to have a KDB.AI account (free tier) and OpenAI API access to follow along with the exercises.
Who is this course for?
- Software developers interested in building AI-powered applications
- Data scientists looking to enhance their NLP projects with RAG
- AI enthusiasts who want to learn about combining vector databases with language models
- Professionals seeking to implement advanced search and question-answering systems
What do I receive for taking this course?
At the end of this each lesson there is a quiz to complete to test your knowledge.
Once you achieve 100% in all quizzes to complete the course, you will receive a Certificate of Achievement which will live on your profile, or you can download it and share it on social media or LinkedIn.
Course Content
+125
enrolled
Course Includes
- 5 Lessons
- 13 Topics
- 13 Quizzes
- Course Certificate
