Building Retrieval Augmented Generation (RAG) Applications with LlamaIndex: From Basic Components to Advanced RAG Systems

This course offers a comprehensive exploration of the Retrieval-Augmented Generation (RAG) System and the LlamaIndex framework, tailored for individuals seeking to deepen their understanding and practical skills in advanced document handling and application customization. The course delves into the core principles of RAG and LlamaIndex, guiding learners through the nuances of building, evaluating, and optimizing RAG applications. Special emphasis is placed on customizing these applications to manage large volumes of documents effectively, employing LlamaIndex's robust capabilities.

The significance of this course lies in its focus on real-world application and problem-solving. In an era where data management and efficient information retrieval are becoming more important, mastering the RAG system and LlamaIndex framework becomes crucial. This course addresses these needs by equipping learners with the skills to evaluate RAG systems critically, customize applications with advanced techniques like Data Ingestion and Embedding Models, and implement LlamaPacks for rapid application development. By the end of this course, participants will not only understand the theoretical aspects of RAG and LlamaIndex but will also be adept at applying these concepts to improve document management and retrieval in various professional scenarios.

What you’ll learn and how to apply it

By the end of this course, learners will understand the RAG System and the LlamaIndex framework, customize RAG applications, and master advanced approaches for handling a vast number of documents with LlamaIndex.

This course is for you because

The course is ideal for individuals seeking to learn about what retrieval-augmented generation (RAG) is, comprehend LlamaIndex, and develop end-to-end RAG applications using LlamaIndex. It includes customization, evaluation, and advanced methods for enhancing RAG applications.

• You are a Developer/ ML Engineer/ AI Engineer and looking to learn about building RAG applications.
• You are looking to move into an AI Engineer role.
• You want to become more proficient at understanding the LlamaIndex framework.

Prerequisites

• Beginner Python developer
• Has some familiarity with LLMs

Course Materials

• GitHub link