Numan is a UK digital health company helping patients manage long-term conditions across weight, hormones, and sexual health. Many of our patients are on a weight-loss programme and use the app daily to track progress.

When building Nu, our AI health coach, one of our design challenges was delivering high-quality health coaching without crossing the line into medical diagnosis, prescriptions, or making clinical decisions—all strict boundaries for agents in our highly regulated medical space. While we initially focused on the agent and automation around it, what turned out to matter most was the quality of our evaluation criteria. This is the story of how we developed and improved our evaluation criteria to deliver high-quality user experiences while steering clear of areas subject to regulation.

Where standard eval approaches fell short for Nu

We started where most teams start, with a dataset of inputs and expected outputs, and an LLM judge scoring each reply. While that playbook works well for a Q&A agent, coaching is a conversation, not an answer. Every approach that fixed Nu's reply in advance failed in two ways: