LlamaIndex Fundamentals: Build AI Agents and RAG Apps

As large language models (LLMs) become central to modern software, the ability to connect them to private data is a highly sought-after skill. LlamaIndex provides a powerful framework for bridging the gap between raw documents and intelligent AI applications. This course guides you through the process of building smart AI agents and implementing Retrieval-Augmented Generation (RAG) systems. You will start with the foundational concepts of document indexing and vector embeddings, then progress to designing autonomous agents capable of querying complex datasets to provide accurate, context-aware responses. What you'll learn: - Understand the core concepts of LlamaIndex, LLMs, and document indexing. - Build foundational Retrieval-Augmented Generation (RAG) pipelines to query your own data. - Integrate modern vector databases for efficient semantic search and retrieval. - Design intelligent AI agents capable of multi-step reasoning and tool use. - Apply basic prompt engineering techniques to improve agent accuracy and reliability. - Practice structuring and chunking complex documents for optimal AI consumption. The curriculum flows logically from essential terminology and basic data ingestion to advanced agentic workflows. Through text-based explanations and practical code snippets, you will gradually build the skills needed to develop robust AI applications from the ground up. This course is designed entirely for beginners and aspiring developers; no prior machine learning experience is required. Enroll today to start building your own data-driven AI agents with LlamaIndex.