TensorAPI.comLabels with decision rules
Design classification decisions that remain reviewable
A classification workflow turns an input into a decision under a defined label system. The difficult questions are often about that system: whether labels overlap, when the model should abstain, and what a numerical score means. This guide helps you design the application contract around those choices. It applies to dedicated classifiers and prompted language models while keeping their capabilities distinct. Begin with the routing or categorization task, then evaluate the whole decision process before allowing a score to control downstream behavior automatically.
Define labels and abstention separately
Give each label an inclusion rule, an exclusion rule, and a small set of examples. Decide whether the task chooses exactly one label or permits several. Overlapping categories require explicit precedence rules or a multilabel contract; the model should not be responsible for inventing that policy.
Keep a versioned vocabulary with the output. A score vector depends on its column-to-label mapping, and a generated label must belong to the allowed set. Preserve input identifiers so batched results remain attached to the correct records.
Use a separate review or unavailable state when the system cannot make an accepted decision. A general category describes content, while abstention describes the decision process. Combining them hides uncertain cases and makes it harder to measure how much work the automation actually completes.
Choose thresholds from observed tradeoffs
A model score may rank examples without representing a calibrated probability. Normalizing scores does not by itself establish calibration, and a generated confidence statement is not a measurement of correctness. Name the score according to its validated interpretation and state where comparisons are meaningful.
Evaluate proposed thresholds on representative examples with trusted labels. Measure both the quality of accepted decisions and coverage, the fraction handled automatically. Inspect each class because one global threshold can conceal different error patterns.
| Decision concern | Inspect |
|---|---|
| Wrong automatic assignments | Precision among accepted decisions |
| Relevant cases missed | Recall for each class |
| Review workload | Abstention rate and queue capacity |
Choose the operating policy based on the task's consequences and available review process. Reevaluate it when the model, label definitions, or input population changes.
Keep validation and execution failure visible
Validate the response before acting: required fields, valid labels, correct item identifiers, and consistent decision states. For generated outputs, check that an explanation has not introduced an unsupported label or changed the routing rule. Treat the surrounding application as the authority for what action follows a result.
Distinguish a valid abstention from a timeout, invalid response, or unavailable model. A failed classification is not evidence that a record belongs to the general class. Retain these states in quality reports instead of dropping difficult cases from the denominator.
Use reviewed samples to compare deployment behavior with the evaluation set. Examine ambiguous inputs, short records, and underrepresented sources. When reviewers disagree about the expected label, refine the definitions and annotation guidance before assuming that another model will resolve the underlying ambiguity.
Use the official reference for the documented interface; the workflow recommendations above are Tensor API Lab guidance.