Introduction to the Course

Welcome to the exciting world of retrieval-augmented generation (RAG)! Think of RAG as a powerful tool that enables large language models (LLMs) to produce text that’s not only coherent but also factually accurate. In this course, you’ll learn how RAG works and how to use it effectively in your projects. Ready to unlock RAG’s full potential? Let’s get started!

What is RAG?

At its core, RAG is a clever way to make LLMs smarter by pairing them with information retrieval systems. Imagine your LLM as a brilliant but forgetful student—knows a lot but doesn’t always remember the details. Now, pair that student with a library (the information retrieval system), and suddenly they’re unstoppable!

Here’s how RAG works:

• Information retrieval: Searches through a vast knowledge base to find documents relevant to the query.

• Large language model (LLM): Combines the retrieved information with its internal knowledge to generate informative and contextually accurate responses.

Together, these components tackle one of AI’s biggest challenges: factually incorrect or misleading information.

What should you know before starting?

Having some basic knowledge will help you get the most out of this course:

• NLP fundamentals: Concepts like tokenization, stemming, lemmatization, and embeddings

• LLMs: A general understanding of how large language models work, including their strengths and limitations

• Information retrieval: Familiarity with indexing and retrieval techniques

Are you new to RAG? We recommend starting with our Fundamentals of Retrieval-Augmented Generation course for a solid introduction to the basics before diving into advanced techniques.

About this course

We’ll begin with the basics and progressively move into advanced RAG techniques. By the end of this course, you’ll have mastered:

• The core principles of RAG, including how retrieval and generation work together

• Different approaches to implementing RAG include naive, advanced, and modular methods

• Selecting the best RAG approach for your specific application

• Pre-retrieval optimization techniques, like indexing and query formulation, to enhance your RAG system

• Post-retrieval techniques, like RAG-Fusion and cross encoder reranking, to refine your results

What can you do with RAG?

RAG unlocks incredible opportunities to create smarter, more reliable AI systems by combining information retrieval with powerful generative models. With RAG, you can:

• Build chatbots that deliver factually accurate responses.

• Generate informative summaries of complex topics.

• Develop question-answering systems grounded in reliable knowledge.

Whether tackling business problems or advancing AI capabilities, RAG equips you with the tools to build intelligent, trustworthy solutions.

Who is this course for?

This course is perfect for anyone eager to harness the potential of RAG. If you’re:

• NLP enthusiasts curious about cutting-edge techniques

• Machine learning practitioners integrating RAG into workflows

• Developers building smarter, AI-powered applications

• Simply excited to explore advanced text-generation methods

You’re in the right place to master RAG and apply it to real-world challenges.

This course is logically structured to guide you step by step. We’ll start with the fundamentals of RAG and identify the issues in naive RAG, then gradually progress toward advanced techniques. Each chapter builds upon the previous one, ensuring a comprehensive learning experience.

Get ready to explore the possibilities of RAG-powered systems! By the end of this course, you’ll be equipped to generate informative and factually sound text, opening doors to innovative applications in AI.