neuron-evaluation-engineer

To run AI evaluations, you implement a BaseEvaluator with a dataset, define run and evaluate methods, and execute the evaluation to generate a results summary. This structured workflow manages setup, run, and evaluate phases to validate AI performance.

Dataset-driven testing for AI systems uses Array and JSON datasets to validate model outputs against predefined assertions. This approach ensures consistent evaluation across multiple test cases within an automated workflow.

You create custom assertions by defining evaluate methods within an evaluator class to compare AI outputs against expected results. These assertion-based validations are applied across loaded datasets to measure performance accuracy.

Yes, the evaluation workflow supports JSON datasets alongside Array datasets for dataset-driven testing. These formats are loaded into evaluators to systematically run assertions and analyze AI performance results.

Evaluation results are captured using configurable Output Drivers to record outputs generated during the run phase. These drivers enable subsequent result analysis by providing a structured summary of the assertion outcomes.

Before testing AI performance, you must define evaluators, prepare Array or JSON datasets, and configure output drivers. These components establish the structured workflow required to execute the setup, run, and evaluate phases.