Building RAG Pipelines with LlamaIndex

Standard large language models often lack access to your specific, private data, leading to incomplete or generic answers. Retrieval-Augmented Generation (RAG) solves this by connecting models to external documents, ensuring accurate, context-aware responses. In this text-only course, you will learn how to use LlamaIndex to build, optimize, and deploy robust RAG pipelines. You will transition from understanding basic LLM limitations to implementing advanced semantic search, document ingestion, and custom query engines. What you'll learn: - Understand the core concepts of Retrieval-Augmented Generation and vector embeddings - Ingest, parse, and chunk diverse document formats using LlamaIndex data connectors - Configure vector stores and index your data for efficient similarity searches - Build custom query engines to retrieve precise context for large language models - Implement advanced retrieval techniques like reranking and hybrid search - Evaluate and fine-tune your RAG pipeline's retrieval accuracy and response quality You will start with foundational AI search concepts and step-by-step setup instructions. From there, you will progress through practical text-based exercises to construct, refine, and evaluate complete retrieval pipelines. This course is designed for developers, data enthusiasts, and software engineers who are new to LlamaIndex and want to connect AI models to custom data sources. No prior experience with vector databases is required. Start reading today to unlock the power of context-aware AI search in your applications.