Eugene Yan's AlignEval is an app designed to make LLM evaluation more approachable by turning it into a structured workflow. The process begins with looking at and labeling your data, then building an LLM-evaluator on top of those labels. The underlying idea is that evaluation should be grounded in real outputs rather than generic metric templates.
After the evaluator is built, AlignEval has users evaluate the evaluator itself: compare its judgments with the human labels and optimize it accordingly. That closes the loop, with labels serving as the source of truth and iteration improving alignment. The title's promise of making evals "easy, fun, and automated" points at lowering the barrier for teams that know they should evaluate but find current tooling too tedious.
With only one source available, there are no differing perspectives to compare here. The main takeaway is practical: treat evaluation not as a one-time test but as a system you build, validate, and refine.