Instructions
AI quality is slippery because it's multidimensional. Accuracy is one dimension — but a technically accurate output that's formatted wrong, too verbose, or misses the user's actual intent is still a failure. Before you can test your copilot or improve it over time, you need a clear definition of what quality actually means in your specific use case.
The common mistake is measuring what's easy to measure rather than what actually matters. Builders track things like response time, error rates, and token counts — which are useful — while never defining whether the AI outputs are actually good for users. This leads to a product that works technically but underperforms in practice, and to an improvement process that's flying blind.
Defining quality well means thinking from the user...
