Ship evals before you ship features
You can't improve what you can't measure. In LLM systems, measurement is the hard part.
The standard product development loop — build, ship, measure, iterate — breaks down for LLM features because the 'measure' step is non-trivial. You can't A/B test output quality without defining what quality means, and defining what quality means for open-ended generation is genuinely hard.
The teams that iterate fastest on LLM products are the ones who invest in evaluation infrastructure before they need it. Not after they've shipped something and noticed it's bad. Before — as part of the initial build.
Concretely: before you ship a new LLM feature, you should have a test set of representative inputs, a definition of what a good output looks like for each, and a way to run that test set automatically and get a score. The score doesn't need to be perfect. It needs to be consistent enough that a regression is distinguishable from noise.
The evaluation investment feels slow at the start. It pays back every time you change a prompt, swap a model, or modify a pipeline stage and need to know whether things got better or worse. Without it, you're guessing.