eval-suite

Generate structured evaluation datasets by providing a requirements description to produce golden examples, adversarial inputs, boundary cases, and regression anchors for prompt testing. The suite outputs rubric-based scoring in binary terms to measure AI outputs against defined criteria.

Regression anchors are stable baseline test cases included in an evaluation dataset that prevent AI output degradation by verifying performance remains consistent during feature evolution. They provide binary rubric-based scoring to flag regressions before rollout.

Binary rubric scoring for evaluating AI classifiers and pipelines is supported by generating structured test data with golden examples and adversarial inputs. This allows you to measure outputs against explicit pass/fail criteria during validation workflows.

The best way to create adversarial inputs and boundary cases for AI evaluation is to define your requirements and let the generator automatically construct these edge cases. This reduces reliance on ad hoc testing by systematically covering boundary conditions.

The generated evaluation data integrates with existing baseline and prompt-test workflows to quantify performance. It applies the structured datasets to prompts, classifiers, and pipelines, delivering rubric-based scoring results directly into your validation process.

You need golden examples in an AI evaluation suite when establishing standard reference outputs to measure model accuracy against explicit rubrics. They serve as ideal baseline responses for end-to-end evaluation planning before feature rollout and during feature evolution.