Build a testing environment that evaluates whether different AI models or prompt configurations consistently produce outputs compatible with a required schema.
Users define a JSON Schema or equivalent structured contract, a collection of test prompts, expected constraints, and outputs generated by different models.
The application should:
Validate syntax and schema compliance.
Detect missing required fields, unexpected fields, invalid enumerations, and incorrect data types.
Test edge cases involving empty values, long strings, Unicode, nested structures, and ambiguous instructions.
Compare model outputs against semantic acceptance criteria supplied by the user.
Measure format compliance separately from factual correctness and task quality.
Track regressions across prompt versions and model configurations.
Generate minimal failure examples and suggest prompt revisions.
Export a reproducible test suite for continuous integration.
Provide a schema editor, test-case manager, output diff viewer, and compatibility dashboard.
Never assume that valid JSON means the content is correct. Preserve raw model outputs, redact secrets, and distinguish deterministic validation failures from subjective semantic judgments. Do not claim one model is universally more reliable based on a small or unrepresentative test set.
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