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AI RELIABILITY & EVALUATION LAB

Test AI.
Then decide to trust it.

Test AI Before You Trust It.

One convincing answer is a starting point. Build a test set, challenge two versions, and find the failures that matter before your users do.

Start an investigation →Learn evaluation
01   Define what good meansInputs · references · observable checks
↓ run the same tests
Version AVersion B
↓ inspect the difference
02   Make an evidence-based decisionNo mystery score. No automatic winner.
Local-first · no account requiredSaved responses work without AI. Live calls require a configured server and your approval.
01 / DEFINE THE CONTRACT

What should your AI do—and never do?

Human & AI-judge rubric

Each criterion has a name and anchored description. Reviews remain separate from automated pass/fail.

02 / BUILD REPRESENTATIVE TESTS

Test the ordinary. Probe the unexpected.

Six original practice collections contain 54 tests across nine categories. Their A/B responses are authored demonstrations, not model executions.

Test case editor

Schema subset: type, properties, required, items, enum, additionalProperties, numeric/string/array bounds. Other keywords produce errors. Regex runs in a time-limited worker.
Saved responses & observable traces

Events: tool, handoff, validation, approval, outcome. Imported traces are unverified records of observable actions, never hidden reasoning.

03 / RUN THE EXPERIMENT

Same tests. Two versions. Visible evidence.

Version A
Version B

Saved mode grades the responses in each test. Repeated saved samples reuse the same text and are not independent evidence. Live retries may incur additional charges. Cancellation cannot recall a request already accepted by a provider.

Ready to test.

04 / INVESTIGATE THE EVIDENCE

Find the failure behind the number.

How these metrics are calculated

Pass rate: passing test-samples / completed test-samples, including errors. Failure rate excludes execution/grader errors, which are reported separately. Format compliance: passing schema/field checks / all schema/field checks. Latency is measured client request time for successful live attempts, excluding earlier retries; saved-response latency is unavailable. Tokens are only those reported by the provider for successful attempts. Costs use your entered rates and exclude failed attempts, caching and tiered pricing. Samples are not guarantees of future behavior.

Performance by category

Run history · automated pass rate

Failure clusters & suggested experiments

Select a result to inspect its response, criteria and source context.

05 / COMPARE & DECIDE

What improved? What broke?

Gates: all critical tests pass; no failed tool-workflow checks; no high-priority regression; complete comparable evidence; optional latency limit. These are automated preconditions, not a deployment approval or safety guarantee.

Choose two runs from the same dataset to begin.
06 / BUILD YOUR EVALUATION SKILLS

Ten small experiments. Better judgment.

Connection & privacy

Projects, dataset versions, append-only run records and reviews are stored in IndexedDB in this browser profile. Export backups. This is not a multi-user audit service; anyone with this browser profile or developer tools can access or alter local storage.

Live execution and AI judges are unavailable until configured.

Only approved live runs send project instructions, test inputs and context to Gemini. Judge calls also send actual responses and rubric. Imported text is untrusted and never executed. Credentials stay on your server. No analytics are enabled.

Do not send confidential records, passwords or personal data to a model without authorization. The included gateway supports Gemini; additional providers require a server-side adapter. Arbitrary external endpoint URLs cannot be entered here.