What we learn building and testing AI systems with expert people, written down for teams doing the same.
Public benchmarks tell you how a model does on someone else's questions. Here is how to build tests from your own work, probe for safety gaps, and decide when a model is ready to ship.
A working demo is not a finished agent. Here are the four things we check, from a clear task to a way to know when it breaks, before an agent goes in front of real users.
Models learn from examples, but what counts as a better answer is a human judgement. A look at how expert comparisons turn taste and domain knowledge into a training signal.
