Evaluating Multi-Turn LLM Conversations and Chatbots

Building conversational AI is easy, but ensuring a chatbot remains coherent and helpful across multiple turns of dialogue is incredibly challenging. This course teaches you how to systematically analyze, test, and improve multi-turn Large Language Model (LLM) interactions. You will transition from manual, ad-hoc testing to structured, automated evaluation strategies. By understanding how to identify conversational drift, context loss, and hallucination in multi-turn dialogues, you will be able to design reliable AI systems that maintain high performance over long user sessions. What you will learn: Understand the foundational mechanics of multi-turn dialogues, context windows, and state management in LLMs; Identify common failure modes such as conversational drift, topic switching errors, and memory decay; Apply the LLM-as-a-judge pattern and other modern evaluation frameworks to automate quality checks; Create robust test suites and synthetic datasets to simulate realistic multi-turn user interactions; Evaluate Retrieval-Augmented Generation (RAG) performance in multi-step conversational contexts; Debug complex conversation traces to pinpoint exactly where an interaction went off track. The course begins with foundational concepts of conversational state and dialogue history before moving into practical evaluation methodologies and automated testing workflows. You will read through clear explanations and structured code examples designed to help you implement these evaluation frameworks immediately. This course is designed for software developers, product managers, and AI enthusiasts who are new to LLM evaluation and want to build more reliable conversational agents. No prior experience with AI testing is required. Start reading today to build conversational AI systems you can truly trust.