curraiLLM evals
Run LLM evals on the outputs your users actually see
Currai helps teams turn production traces into quality checks, score model outputs, and compare prompt versions before regressions become product issues.
Evaluate real traffic, not stale examples
Offline datasets are useful, but LLM products change as users discover new edge cases. Currai starts from production traces so your evals reflect real prompts, real failures, and real cost tradeoffs.
Use eval scores to measure quality alongside token usage and latency, then compare whether a prompt or model change improved the right metric.
- Score traced outputs with LLM-as-a-judge or custom rubrics.
- Compare prompt versions with production A/B tests.
- Review quality, cost, and latency together before shipping changes.
Keep evals tied to observability
Currai keeps the raw trace, the output, and the score together. When a score looks wrong, you can inspect the prompt, model response, tools, metadata, and surrounding session instead of hunting through separate systems.
