Resources

Documentation

Practical guidance for designing expert-grounded benchmarks, eliciting correctness, and carrying both into AI development.

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Guides

From benchmark intent to repeatable development practice.

Use these guides to structure coverage, capture expert standards, and preserve evidence across the AI development lifecycle.

Start here

Design benchmark coverage deliberately

Learn how requirements, internal materials and existing cases become Dimensions, Topics and Case Construction Patterns.

Coverage Engineering

Expert judgment

Make correctness executable

Structure specialist preferences, exceptions and disagreements into policies, applicability conditions and binary rubrics.

Correctness Elicitation

Development loop

Carry standards into every trial

Connect cases, trajectories and rubric results across harness experiments and ongoing improvement.

Trialground