Model Capability Definition & Evaluation
Model Capability Definition & Evaluation
Evaluation is not a score at the end of a project. It defines capability first, exposes failure, and then verifies whether data and model iteration produced real improvement.
Start from the customer capability problem
- Clarify the capability object, usage scenario and failure cost.
- Build a baseline for the current model or workflow and separate known failures from unknown risks.
- Turn vague quality judgments into observable and decidable tasks and metrics.
Evaluation system design
- Design benchmarks and hard-case structures that cover real capability boundaries.
- Write rubrics as understandable, auditable rules that can handle exceptions.
- Combine human evaluation, rule checks and verifiers according to task risk.
- Control evaluation drift through dual review, arbitration and consistency tests.
Data and model iteration validation
- Design training, preference or evaluation data plans based on failure categories.
- Keep independent evaluation sets to avoid contamination between production and validation data.
- Compare version changes and identify improvement, regression and structural bias.
- Feed failures into the next round of task, data and model iteration.
Capability boundary
- Executing customer-provided rules is not the same as defining model capabilities.
- Do not claim capability evaluation without evidence of real task definition, evaluation design or failure analysis.
- Evaluation conclusions apply only to the defined samples, tasks, versions and criteria.