> Overall the evaluation of success was one of the most challenging parts of the project. As a developer, I’m used to building features that either work or don’t and there is often an objective way to measure how well a feature performs. For messy real world data it was hard to evaluate how good or bad the pipeline was. Furthermore, it was easy to start optimising for a specific parameter or route and find later that this work led to severe degradations in other areas.

> Verification becomes hard to reason about because there is no ground truth for points of interest, there are no red/green unit tests for taste. I’m sure these are familiar challenges to data scientists and that there are frameworks and evals for working on them. This will require more iteration and manual overrides. Hopefully with feedback and collaboration from the community. But for now I’ve shipped V1…

I suspect LLMs may be able to help us quantify our taste because they can keep track of so many data points all at once, where we have to lossily abstract these details away.