AI testing and evaluation

Episode Description: In this episode, we dive into a practical, three-step approach to transform LLMs from unpredictable evaluators into reliable and transparent tools. Stop relying on vague instructions like "evaluate relevance" and learn how to implement a high-precision framework that yields consistent results.What we cover in this episode:• Step 1: The Power of Binary Criteria. Learn why you should define 5–7 concrete evaluation metrics—such as checking for fabricated facts, length limits, or specific tones—that result in a simple "yes" or "no".• Step 2: Structured Output for Accountability. Discover how to request JSON or other structured formats so the model provides a verdict and the specific evidence or justification supporting its decision.• Step 3: Continuous Improvement and Debugging. We discuss the importance of running 20–30 test examples to identify where the model makes mistakes. We explain why evaluation failures often stem from how criteria are formulated rather than the model's inherent capabilities.Tune in to learn how to move away from "black box" scoring and create an evaluation logic that you can continuously improve and fully understand.