Gen AI - RAG Application Development using LlamaIndex

Overview

Join our course on developing retrieval-augmented generation (RAG) applications using LlamaIndex and Large Language Models (LLMs). This comprehensive course covers integrating LlamaIndex with diverse data sources and fine-tuning prompts for cutting-edge AI-driven applications.

Begin with understanding the fundamentals of LLMs and key prompt engineering concepts before delving into LlamaIndex's extensive capabilities.

Learn environment setup and create your first application, progressing through various prompt types, including conversational and semantic similarity evaluators.

Discover the importance of language embeddings and efficiently manage data with vector databases, Chroma DB, or SQL databases. You'll also learn to create and optimize query pipelines like sequential and DAG pipelines, working with agents and tools to build powerful real-world applications.

The course includes practical projects such as developing a calculator using a ReAct agent and a document agent with dynamic tools, showcasing LlamaIndex's versatility across different use cases.

Designed for developers, data scientists, and AI enthusiasts, this course requires a basic understanding of Python programming and AI concepts, elevating your expertise in advanced application development with LlamaIndex.

Upon course completion, confidently design, build, and deploy RAG-based applications tailored for complex, real-world data challenges.

Subjects