Braintrust: Evaluating LLM Quality
A platform for logging, evaluating, and comparing LLM model performance to optimize accuracy and cost.
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.