Scorecard

Scorecard is a platform for building, evaluating, and iterating on LLM-powered applications in a structured, measurable way. It helps teams move beyond ad-hoc prompt tweaking by providing a repeatable framework to define quality, run tests, and track performance over time. The primary purpose of Scorecard is to make AI behavior more predictable and aligned with product requirements, even as models, prompts, and data change.

The tool allows you to define evaluation criteria as “scorecards” that capture what good output looks like for your use case—such as accuracy, tone, safety, and adherence to instructions. You can run these evaluations automatically across prompts, models, and versions of your app, using a mix of human-written rubrics and LLM-as-judge scoring. Scorecard supports side-by-side comparisons, regression testing, and experiment tracking so you can see how each change impacts quality. It also centralizes results and metrics, making it easier for teams to collaborate, review outputs, and standardize evaluation practices.