Building Agentic RAG Systems: Design an AI Research Assistant

Standard search engines and basic retrieval pipelines often fall short when dealing with complex, multi-step research queries. By upgrading to an agentic RAG system, you can build AI assistants that dynamically choose their own tools, reason through problems, and deliver deep synthesis. This text-based course guides you through the process of conceptualizing, designing, and implementing a multi-tool agentic research assistant. You will transition from understanding basic retrieval to structuring complex agent workflows that can query vector databases, call external APIs, and self-correct. What you'll learn: - Understand the foundational architecture of Retrieval-Augmented Generation (RAG) and how agentic workflows differ from standard pipelines. - Configure LlamaIndex to manage document ingestion, indexing, and vector database storage. - Design specialized tools and query engines that your AI agent can dynamically select and execute. - Implement routing and decision-making logic to handle complex, multi-step research questions. - Apply modern prompt engineering patterns to guide the agent's reasoning processes and prevent hallucination. - Evaluate the retrieval quality and response accuracy of your agentic system using systematic testing methods. You will start by exploring core RAG concepts and LlamaIndex fundamentals before moving step-by-step through tool creation, agent routing, and conversational memory integration. Through clear written explanations and practical code walkthroughs, you will build a complete blueprint for a functional AI research assistant. This course is designed for developers, data enthusiasts, and AI beginners who want to move beyond simple chat interfaces. No prior experience with agentic frameworks is required, though a basic familiarity with Python is helpful. Start reading today to design your own intelligent, self-routing AI research assistants.