An eval harness is the most underrated piece of an AI system. It’s the difference between iterating with confidence and shipping prompts on vibes.
Start with the behavior you want, not the metric. ‘The chatbot should refuse out-of-scope questions politely’ is a behavior. ‘90% accuracy on the eval set’ is a metric. The metric only means something once the behavior is well-specified.
Build the dataset by hand for the first 100 examples. We use Excel, Notion, or Linear — whatever the team writes in. We label the inputs and the expected outputs. We tag each example with the behavior it’s testing.
Scale the dataset to 500–2,000 examples using synthetic generation, with humans reviewing every synthesized example. Synthetic-only eval sets drift toward whatever bias the generator has — human review is the corrective.
Run the eval on every prompt change, every retrieval change, every model change. Score automatically where you can, manually where you must. Track results over time so you can spot regression.
The eval harness is also your launch readiness gate. We don’t ship to production traffic until the eval scores cross a threshold we agreed on with the customer in week one.
AI without evals is software without tests. Sometimes it works. Often it doesn’t. You won’t know until your customers tell you, and by then it’s already cost you something.
Written by the brainiac/studio team. We publish original work from the engineers, designers, and marketers who do the work — never outsourced to a content shop.