AI models only know what they have been trained on - mostly "free" information from the internet such as articles, API documentation, books, and much more. But what if you need the AI model to understand data beyond its training corpus?
In this course, you will master the approach of Retrieval Augmented Generation (RAG), which allows you to extend the capabilities of LLM with your own data. You will learn how embeddings and vectors work (and how RAG uses them to create a cohesive context), how to automate data storage and integrate it into your custom RAG, and how to apply this approach most effectively in real code bases.
