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Jev text classification and task routing: labels to fallbacks

Classification is useful because of what happens after the label. Define the queues and their actions before collecting examples or optimizing a headline accuracy number.

Jev Gallery · Reviewed

Define distinct queues

For product feedback, begin with bug report, feature request and other, each with an observable definition. Do not mix urgency, feature area and sentiment into one single-choice dimension.

Choice selects from a fixed option set. For overlapping topics, consider separate condition questions combined in code; if one label is required, define how to select the main intent.

Source: TypeSafe · Choice ↗

Build an inspectable workflow

These are editorial implementation suggestions. Thresholds need validation against your own examples.

  • Label a small representative set of synthetic or authorized inputs.
  • Store model labels as suggestions before moving source records automatically.
  • Inspect other, uncertain and incorrect results; refine category definitions.
  • Enable automatic routing gradually for validated, low-risk classes.
  • Send missing fields and API failures into an explicit pending queue.

Keep confidence separate from accuracy

Choice confidence describes the concentration of its option distribution. It is not an independently measured accuracy guarantee for an individual result.

Track mistakes by class, review-queue volume, manual corrections and usage per batch. These reveal issues that a single overall accuracy figure can conceal.

Source: TypeSafe · Confidence ↗

Projects that illustrate the building blocks

Jev Codex Router explores task-to-model selection. pg-jev brings semantic judgments into SQL. jev-workbench organizes versioned decision services. They are engineering references at different layers, not ready-made universal support routers.

Inspect how options are created, how batches are assembled and how failures are returned. For database experiments, begin with sample copies rather than sending an entire dataset to an external API.

Maintain an error ledger

Record concrete failures: ordinary requests escalated, incomplete inputs forced into a class, or new categories with no destination. Assign each a fallback action and a traceable record so later instruction changes have a meaningful comparison.

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