Traditional ML told you what to measure. LLMs don’t.

It’s never been easier to build a prototype that looks like it works. You connect to an LLM API, build a demo around it, and people are impressed.

But a demo that looks good doesn’t show that the system works on real inputs.

With traditional machine learning, evaluation was a normal part of the process. The model was trained for one narrow task, so you already knew what a correct answer looked like and how to measure it.

With LLMs, it’s much harder to know what to measure. The outputs are open-ended, and they can fail in ways you can’t predict. Many teams value evaluation but skip it because they don’t know where to start. As a result, they launch with no baseline, so they can’t tell whether later changes help or hurt their use case.

Evaluation doesn’t stop when you deploy to production. Production is the first place where you can measure whether the system helps users with their actual work.

Without evaluation, the first demo is as good as your product will ever get.