Braintrust

Create a project and assemble a small evaluation dataset from representative real or synthetic examples. Define the expected behavior and metrics that reflect product quality, then run the current prompt or model configuration as a baseline. Test one meaningful change at a time so the comparison is interpretable. Inspect individual failures instead of relying only on an aggregate score. Add production tracing after the offline workflow is useful, and periodically promote anonymized failure cases into the evaluation set so future changes are tested against problems users have actually encountered.

For a first evaluation, keep the scope small enough that you can compare the AI-assisted result with a known baseline. Record the configuration, model or workflow choices that produced the result so successful tests can be reproduced. If the product connects to external systems or private data, grant the minimum permissions required and review the provider's current security and data-handling documentation before expanding access.