Evaluate AI workflow accuracy with a labeled fixture
A plausible answer is not a measurement. To evaluate a workflow, build a fixture with known outcomes and decide which errors matter before running the model.
Score the operational result
For classification, compare the assigned category with a label. For extraction, compare individual fields and required relationships. For summaries, use a rubric covering factual support and omitted information. A single overall score can hide a dangerous failure mode, such as accurately extracting most fields while repeatedly getting payment amounts wrong.
- 1Include ordinary, rare, ambiguous, and incomplete inputsInclude ordinary, rare, ambiguous, and incomplete inputs.
- 2Write expected results independently of model outputWrite expected results independently of model output.
- 3Break down errors by field, category, and…Break down errors by field, category, and downstream consequence.
Try it on a small example
- Include ordinary, rare, ambiguous, and incomplete inputs.
- Write expected results independently of model output.
- Break down errors by field, category, and downstream consequence.
What to verify
Keep a separate holdout set when tuning prompts or taxonomies so improvements are not limited to examples you repeatedly inspect. Record the configuration and model for each evaluation. Baleybots' repository includes benchmarks with known design outputs and probes with written answers. Apply the same principle to your own workflow: measure the behavior you need, and preserve failed examples as regression evidence.
Product details checked against the Pipeline architecture on October 4, 2026. These guides describe documented behavior; availability depends on your account and the deployed service. Baleybots is in invite-only beta.