A practical blueprint for evaluating conversational AI at scale

This post details the development of a standardized evaluation process for conversational AI systems, treating every experiment like production code. It covers curating datasets (public and internal), defining actionable metrics using LLMs as judges, and setting up an evaluation platform (Braintrust) to manage datasets, scorers, experiments, and automation. The goal is to ensure accuracy and reliability in AI outputs, extending to various modalities beyond text.