Evaluation runs can be logged programmatically and integrated into your CI/CD workflows via our SDK, making it easier than ever to test and evaluate your LLM apps.
Get detailed visibility into your entire LLM pipeline across your run, helping you pinpoint sources of regressions in your pipeline as you iterate.
Define your own Python validators and LLM evaluators to automatically test your AI pipelines against your own criteria and guardrails.
Save and version all evaluation runs to create a single source of truth for all experiments and artifacts, accessible to your entire team.
Capture underperforming test cases from production and add corrections to curate golden datasets for continuous testing.
We automatically parallelize requests and metric computation to speed up large evaluation runs spanning thousands of test cases.