Defining Evaluation Metrics and Test Frameworks
Explore how to define meaningful evaluation metrics across accuracy, latency, cost, safety, and security for AI solutions. Understand building systematic test frameworks using mixed methodologies like exact-match, LLM-as-judge, and human review. Learn to design stratified datasets that reflect real-world scenarios accurately and avoid common pitfalls. This lesson helps you assess changes with objective metrics, ensuring robust, measurable system quality before deployment.
Atlas’s architecture is integrated, and it now has the observability to catch a regression once it is running in production. This chapter asks an earlier question: before something ships, or before a change goes out, how do you actually know it is good enough? Observability watches a live system for drift after the fact. Evaluation is the deliberate, systematic measurement that answers whether a system, or a specific change to one, clears its bar in the first place, against a built dataset and defined metrics, not against a general impression that it seems to be working. In this lesson, we will cover: ...