Introduction to the Course

Explore the world of retrieval-augmented generation (RAG) and unlock its potential for building powerful NLP applications.

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Welcome to the exciting world of retrieval-augmented generation (RAG)! In this course, you’ll learn how to harness RAG’s power and apply it effectively in your own projects.

What is RAG?

RAG is a cutting-edge technique that empowers large language models (LLMs) to generate more informative and factually consistent text. It works by combining the strengths of two key components:

  • Information retrieval: This component searches a vast knowledge base to identify documents relevant to the user’s query or prompt.

  • Large language model: This component utilizes its internal knowledge and the retrieved information to generate a comprehensive and accurate response.

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The RAG workflow
The RAG workflow

Educative Byte: By grounding the large language model (LLM) in relevant external knowledge, RAG helps address one major limitation of LLMs: their tendency to generate factually incorrect or misleading information.

About this course

In this course, you’ll start with the fundamental concepts and progress to advanced techniques, covering key concepts. Your learning journey will include:

  • The core principles of RAG

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

  • Selecting the most suitable RAG approach for your specific application

  • Advanced RAG techniques for pre-retrieval optimization (indexing and query formulation)

  • Post-retrieval techniques to refine the retrieved information and enhance the final output

By the end of this course, you’ll be well-equipped to leverage RAG for various tasks, such as:

  • Building chatbots with improved factual accuracy

  • Generating informative summaries of complex topics

  • Creating question-answering systems with a strong foundation in factual knowledge

Intended audience

This course is designed for those who have a basic understanding of NLP and are eager to learn more about LLMs and how they work. It’s designed for a wide range of learners, including:

  • NLP enthusiasts

  • Machine learning practitioners

  • Developers interested in building AI-powered applications

  • Anyone eager to learn about cutting-edge techniques in text generation

Prerequisites for this course

A foundational understanding of the following concepts will prove beneficial for a smoother learning experience:

  • Basic NLP principles: Familiarity with concepts like tokenization, stemming, lemmatization, and embeddings

  • Large language models: Awareness of the capabilities and limitations of LLMs

  • Information retrieval fundamentals: Knowledge of basic indexing and retrieval techniques

  • Fundamentals of RAG: For an introduction to RAG, including the principles of combining retrieval mechanisms with generative models, visit the course titled Fundamentals of Retrieval-Augmented Generation.

We’ll explore these concepts further throughout the course, so even if your experience is limited, you’ll be equipped with the necessary knowledge to excel.

This course is structured logically, starting with the fundamentals of RAG and identifying the issues in naive RAG, gradually progressing toward advanced techniques in RAG. Each chapter builds upon the previous one, ensuring a comprehensive learning experience.

Get ready to unlock the power of RAG and explore the possibilities of generating informative and factually sound text!