Evaluating AI Outputs: How to Know If Your AI Is Any Good
Building an AI prototype that impresses in a demo is easy. Knowing whether it will perform reliably on real inputs, whether a new prompt or model has made it better or worse, and whether it is safe to put in front of customers is much harder. Evaluation is the discipline that answers these questions, and it is often the difference between AI projects that reach production and those that stall.
This masterclass introduces practical evaluation techniques and demonstrates them live on a working AI application. We build a test set, score outputs using both rules and an AI model acting as a judge, catch hallucinations, and compare prompts and models objectively. Participants leave with a repeatable approach they can apply to their own AI work.
Learning Outcomes
By the end of the session, participants will be able to:
- Understand why evaluation matters: Learn why AI systems need different testing approaches from traditional software, and what “good” looks like for different use cases.
- Build a test set: See how to create representative test cases, including edge cases and adversarial inputs, from real usage.
- Score outputs at scale: Watch rule-based checks and LLM-as-judge scoring applied live, including how to check that the judge itself is reliable.
- Make evidence-based decisions: Use evaluation results to compare prompts and models, catch regressions and decide when a system is ready for production.
Target Audience
This masterclass is designed for hands-on practitioners, including developers, data engineers, analysts and architects, who already use generative AI tools confidently and want to go further.
It is equally relevant to managers and product owners who need confidence and evidence before approving an AI solution for wider use.
Basic coding knowledge will help practitioners get the most from the demos but is not required to follow along.
