Trust & Evaluation

Methodology & Benchmarks

This page explains how Zush evaluates AI file renaming quality for macOS workflows. We publish the rubric, validation flow, and update cadence so recommendations stay reproducible, auditable, and practical for real batch operations.

Monthly reviews 5 weighted checks Public changelog
Governance

Editorial Accountability

Primary author

Zush Editorial Team

Technical reviewer

Zush Product Team

Update cadence

Criteria are reviewed monthly, with material changes logged in the changelog.

Rubric

Scoring Rubric

Each workflow run is graded against a weighted model that balances naming accuracy, operational safety, and day-to-day usability inside a macOS workflow.

Dimension Weight What we measure
Semantic filename accuracy 35% Does the new name describe file meaning, not only generic patterns?
Consistency in batch runs 20% Stable naming style across mixed files in one task.
Automation depth 15% Batch workflow, folder monitoring, and repeatability.
Safety controls 15% History, rollback, preview-before-apply behavior.
Operational fit 15% macOS workflow quality, speed, and setup friction.
Validation

Benchmark Protocol

  1. 01

    Use a mixed-file benchmark set with screenshots, photos, PDFs, and office documents.

  2. 02

    Run controlled rename jobs with identical prompts and naming constraints.

  3. 03

    Score outputs against the rubric and record false positives or ambiguous labels.

  4. 04

    Validate revert and recovery behavior before marking a setup as production-safe.

  5. 05

    Repeat checks after release updates and track material changes in the changelog.