From the course: Building a RAG Solution from Scratch

Getting the most out of this course

From the course: Building a RAG Solution from Scratch

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Getting the most out of this course

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- [Instructor] This course is designed for data scientists and NLP enthusiasts who already have some experience with language models and fine tuning. If you're familiar with transformers, LLMs, and Python, you are in the perfect place. We'll build on that knowledge as we explore each layer of RAG. In this course, you learn how to retrieve relevant information with precision, generate context-aware responses, and, finally, to evaluate your RAG model to ensure it's accurate and responsive. We'll work with TensorFlow, Keras, and Hugging Face, and we will use the MIMIC-III dataset, which provides real-world complexity with anonymized health records. Just as a quick reminder, this course comes with downloadable exercise files, which have two folders, named Assets and Exercise Files. In the Exercise Files folder, you will find the notebooks we will use throughout the course, and the assets needed for those notebooks will be in the Assets folder. Just make sure you pause the video now and download them so they are ready and handy. Think of building a RAG solution like running a library. The retrieval model is a librarian quickly finding relevant information, and the generative model is the expert summarizer, creating useful insights from what's retrieved. By the end of this course, you'll have the tools to create your own chatbot and even more. So get ready to transform the way your models handle information. Before we move on, please pause and take a moment to reflect on the following advice. Let's dive into the world of RAG.

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