AI ReliabilityPRODUCT LLM-EVALUATION
LLM Evaluation
Judges you can calibrate, not just more metrics
LLM-as-judge is widely distrusted, and the documented failure modes are specific: position bias, verbosity bias, self-enhancement bias, prompt sensitivity, and poor correlation with human labels. The answer is not another metric — it is calibration, confidence intervals, and knowing when the judge is unreliable.
What it does5 CAPABILITIES
The capability, stated plainly
- 01
Continuous calibration against human labels
A judge's agreement with human raters is measured continuously rather than assumed once at setup.
- 02
Bias diagnostics
Position, verbosity and self-preference bias are tested for explicitly, because a judge that prefers longer answers is not measuring quality.
- 03
Confidence intervals on every score
A score without an interval invites decisions the sample size does not support. The academic consensus is blunt: a large fraction of reported LLM evaluations are statistically invalid.
- 04
Unreliability detection
When a judge is not trustworthy for a given use case, the system says so instead of returning a confident number.
- 05
Eval suites generated from production traces
The cases that matter are the ones your system actually sees, not the ones someone wrote by hand at kickoff.
Why it lives here
One pipeline, not another agent to install
LLM Evaluation reads the same instrumented stream as everything else on the platform. That means one collector in your infrastructure, one redaction policy applied before anything leaves it, and one usage bill — rather than a separate vendor, agent and contract per question you want answered.