In the guide "Evaluating Multi-Turn Conversations," Abdallah Abedraba explores systematic approaches to evaluate chatbots, emphasizing the complexity of multi-turn interactions where a single incorrect response can disrupt the entire dialogue. Two primary methods are discussed: N+1 Evaluations, which focus on analyzing real user interactions to pinpoint and rectify recurring issues, and Simulated Conversations, which use predefined personas and scenarios to test the chatbot's response to edge cases. The guide advocates for the early implementation of robust evaluation systems to enhance chatbot efficacy, suggesting that teams who invest in such systems can iterate and improve their bots more efficiently. It highlights the importance of tracking progress over time and adapting test datasets as new insights emerge, and it underscores the role of LLMs not only as subjects of evaluation but also as tools to aid the development of these systems.