How to evaluate multi-turn conversations

Evaluating AI chatbots requires a dual-layer approach that combines single-turn and multi-turn scoring to effectively assess both individual responses and entire conversations. While single-turn evaluations focus on aspects like tone, empathy, and adherence to company guidelines for each interaction, they are insufficient for determining whether an AI has satisfactorily resolved a customer's issue. Multi-turn scoring, therefore, is crucial to understanding the overall quality of an interaction by examining whether the customer's problem was successfully addressed across the conversation. Implementing this involves logging conversations as structured data, using AI models like GPT-5 Mini to assess interactions, and setting up automated scoring processes in tools like Braintrust. The integration of automated scoring with features like Topics, which clusters and summarizes conversations into categories, allows for the identification of recurring issues and optimization of AI performance at scale. This comprehensive evaluation framework supports continuous improvement of conversational AI systems by surfacing patterns and guiding engineering efforts towards resolving frequent customer pain points.