Build The AI Model Upgrade Compatibility Bench, an evaluation workspace that helps developers determine whether replacing one language model with another will break an existing AI application.
Allow users to define application tasks, provide representative inputs, enter outputs from the current and candidate models, and specify expected behavior.
The tool should:
Compare output structure, required fields, factual claims, tone constraints, and task completion.
Validate structured responses against JSON schemas.
Detect missing information, unexpected refusals, unsupported assertions, and instruction-following regressions.
Support weighted evaluation criteria with configurable pass thresholds.
Group failures by severity and likely application impact.
Compare latency, token usage, and cost when users supply those measurements.
Produce a side-by-side regression report.
Export a reusable evaluation dataset for future model upgrades.
Include examples for customer support, structured extraction, code generation, and classification. Use deterministic validators wherever possible, and label subjective judgments separately from objective checks. Do not claim that a small sample proves universal model superiority. Make the prototype usable without connecting to any paid model API.
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