Braintrust: Evaluating LLM Quality

Evaluating an AI app is hard because there is no simple "pass/fail" metric. Braintrust allows teams to build "evaluation sets"—a collection of queries with expected answers—and then track how different model versions or prompt changes impact the output quality.

Continuous Evaluation

Braintrust integrates into your CI/CD pipeline, so every time you change a prompt, it automatically runs your test cases. It provides granular performance metrics (accuracy, latency, cost), allowing you to make data-driven decisions about when a new model version is ready for production.

Collaborative Feedback

It provides a centralized dashboard for team members to review LLM outputs and leave "human-in-the-loop" feedback, which is crucial for building high-quality, fine-tuned datasets that improve your model’s reliability over time.