Language-data work depends on precise definitions, representative examples, and a clear record of how a label or model decision was reached. Strawberry can use the Lettria workspace in your browser to organise visible taxonomy, annotation, and evaluation context into a review packet.
It supports accountable language work rather than treating a model output as self-explanatory.
01
Make each label mean one defensible thing.
A taxonomy fails when two reviewers can apply the same label differently without noticing.
Strawberry can inspect the Lettria examples and guidance available in the workspace, then prepare a boundary-case review that makes ambiguity explicit for the language-data owner.
02
Turn disagreement into a guide improvement.
Annotation disagreement is useful evidence when it is tied to the instruction that produced it. Strawberry can compare visible Lettria examples with the annotation guide and organise a targeted revision list instead of reducing disagreement to a single quality score.
03
Check for drift before changing the taxonomy.
A cluster of unexpected classifications may reflect new data, a weak definition, or a problem in the evaluation setup. Strawberry can prepare a Lettria quality brief that separates the pattern observed from the cause that still needs testing.
04
Review language definitions on a cadence that fits the data.
A biweekly pass gives the team a regular place to resolve new edge cases before they become inconsistent training or reporting data. A Lettria skill can preserve the projects and review rubric, while a routine assembles the contested-label queue for the data steward.
Lettria Strawberry