Building RAG Systems with LlamaIndex: Chat with Your PDFs

Transform static documents into interactive knowledge bases using Retrieval-Augmented Generation (RAG). This text-based guide teaches you how to parse, index, and query your PDF files using LlamaIndex alongside powerful language models such as Gemini and Llama via NIM endpoints. You will gain the practical skills needed to design and implement end-to-end question-answering systems over unstructured text, understanding both the theoretical mechanics of vector retrieval and the code required to connect data loaders, indexes, and language models. What you'll learn: - Understand foundational RAG concepts, embedding models, and vector similarity search - Configure LlamaIndex to parse, chunk, and index complex PDF documents - Connect diverse language model backends, including Gemini and Llama via NIM - Implement semantic search and query engines for accurate document retrieval - Apply prompt engineering strategies to eliminate hallucinations and ground responses - Evaluate retrieval accuracy and compare output quality across different model architectures The course begins with foundational concepts and core terminology before progressing into document ingestion, index setup, query optimization, and response synthesis through clear written explanations and structured code snippets. This course is designed for software developers, data practitioners, and technical beginners interested in building AI document pipelines. Basic familiarity with Python programming is helpful, but no prior experience with RAG or vector databases is required. Start reading today and build reliable AI assistants for your personal and professional documents.