Building Conversational Document Q&A Systems with LlamaIndex

Static search engines often fall short when you need to have a continuous, natural dialogue with your business data. By combining modern retrieval-augmented generation (RAG) with context-aware memory, you can transform flat documents into interactive conversational partners. This written course guides you through the process of building intelligent, multi-turn document Q&A systems using LlamaIndex. You will transition from understanding core orchestration concepts to writing clean Python code that processes documents, manages conversational history, and generates precise, context-rich answers. What you'll learn: - Understand the foundations of Retrieval-Augmented Generation (RAG) and conversational AI. - Configure LlamaIndex to parse, index, and query custom documents such as PDFs and text files. - Implement conversational memory to enable multi-turn dialogues that remember past user queries. - Apply prompt engineering principles to refine system responses and summarization quality. - Structure index storage and retrieval pipelines using vector database fundamentals. - Build clean, modular Python pipelines for production-ready document interaction. You will start with essential terminology and foundational RAG architectures before progressing to hands-on code examples. Through reading structured explanations and analyzing step-by-step code blocks, you will learn how to connect documents, memory, and language models into a cohesive system. This course is designed for Python developers, data enthusiasts, and software engineers who are new to LlamaIndex and want to build practical conversational search applications. Basic Python knowledge is recommended, but no prior AI or LLM experience is required. Start reading today to build smarter document assistants that truly understand context.