I'm responsible for multiple LLM apps with hundreds of thousands of DAU total. I have built and am using promptfoo to iterate: https://github.com/promptfoo/pro

I'm responsible for multiple LLM apps with hundreds of thousands of DAU total. I have built and am using promptfoo to iterate: https://github.com/promptfoo/promptfoo https://github.com/promptfoo/promptfoo My workflow is based on testing: start by defining a set of representative test cases and using them to guide prompting. I tend to prefer programmatic test cases over LLM-based evals, but LLM evals seem popular these days. Then, I create a hypothesis, run an eval, and if the results show improvement I share them with the team. In some of my projects, this is integrated with CI. The next step is closing the feedback loop and gathering real-world examples for your evals. This can be difficult to do if you respect the privacy of your users, which is why I prefer a local, open-source CLI. You'll have to set up the appropriate opt-ins etc. to gather this data, if at all.