Building Conversational Chatbots with Chainlit and MCP Servers

Modern AI applications require reliable infrastructure, dynamic context handling, and intuitive chat interfaces. This course guides you step-by-step through developing conversational chatbots powered by Chainlit and Model Context Protocol (MCP) servers. You will gain a clear understanding of LLMOps fundamentals, structured tool integration, and conversational UI development. Through written explanations, code examples, and practical exercises, you will learn how to connect language models to external tools and manage conversational workflows effectively. What you'll learn: - Understand core LLMOps concepts, system architecture, and conversational AI terminology - Build interactive chat interfaces using Chainlit - Configure Model Context Protocol (MCP) servers to provide external tools and dynamic context - Implement conversation state management, session handling, and message history - Apply evaluation and tracing practices to monitor chatbot reliability - Design secure workflows for integrating APIs and local services The course begins with foundational definitions and architecture concepts before progressing to practical implementation patterns, MCP tool creation, and operational best practices. This course is designed for developers, technical professionals, and beginners looking for a direct, text-based path to building modern conversational AI applications. Basic familiarity with Python is helpful but no prior chatbot experience is required. Start reading to build reliable, context-aware conversational applications.