workshop-define-benchmark

To create custom AI benchmarks, you can define specialized scoring functions and structured data extraction handlers within the Benchy framework. This provides a code-first workflow to establish unique metrics and thresholds for model evaluation.

You validate custom AI evaluation tasks by executing smoke tests against your configured model environment. This end-to-end validation ensures the newly defined benchmarks are production-ready and perform as expected.

Yes, defining and testing custom benchmarking tasks requires access to the Benchy CLI. You also need a configured model environment to execute smoke tests and verify task performance during validation.

Yes, you can implement custom weights for schema validity and hallucination by defining unique scoring logic. This allows you to evaluate model extraction performance based on your specific criteria rather than generic default metrics.

Configuring a model environment requires setting up your local model configuration to support the Benchy CLI. Once configured, you can initialize a new task directory and execute smoke tests to verify task performance.

The best way to implement custom scoring logic is by using a code-first workflow to define unique metrics and thresholds. This approach allows you to tailor evaluation criteria specifically for structured data extraction handlers.