Evaluation Framework

Test, score, and compare AI prompts with datasets, batch evaluation, and statistical comparison — so you ship prompts that actually work.

Key Capabilities

Create test cases manually or auto-generate them from your optimization history. Keep every prompt accountable to real examples.

Run prompts against entire datasets at once. Get quality scores, semantic drift, and constraint preservation checks for every test case.

Statistically compare two prompt variants and calibrate LLM judges against ground-truth labels.

Add test cases manually, import from history, or generate them from past optimizations. Each case defines inputs and the expected outcome.

Score prompts against your dataset with LLM judges. Get per-case feedback, aggregate metrics, and pass/fail status in one run.

Compare variants statistically, calibrate scores to ground truth, and iterate on the prompt that performs best.

Make sure a prompt change does not break previously working cases. Run the full dataset before deploying.

Compare two prompt variants head-to-head with statistical significance instead of gut feeling.

Enforce a minimum pass rate in CI/CD. Only promote prompts that meet your quality bar.

Score hundreds of cases in a single run

Configurable scoring against any criteria

See if a variant is truly better

Align scores with ground-truth labels

Build an evaluation suite that proves your prompts are ready for production.