Retrieval-augmented generation (RAG) has quickly become one of the most popular methods for utilizing large language models (LLMs). Why is that? Because RAG addresses a key challenge when using LLMs for specific tasks. Although LLMs are trained on vast amounts of data, they lack access to the specialized information required for personal or business purposes, which increases the likelihood of generating incorrect or "hallucinated" answers. This is where RAG proves valuable. It employs embedding models and vector databases to organize your data so it can be used as context for LLMs. This course, which offers a U365 Micro-Credential for your Career (MCC), explains the components of an RAG application, guides you on how to use them, and teaches you how to build your own RAG app from scratch using Python. Additionally, the course incorporates GitHub Models to enhance your learning experience. Micro-Credentials for your Career (MCC) : 1 US Academic Credit (1.5 ECTS)