Use cases for expert AI data programs

These pages describe the work an expert cohort can run. They are not job listings and not a public catalog of named clients.

• LLM Evaluation by Domain Experts

  LLM evaluation measures whether a model’s generated answers are correct, useful, safe, and acceptable for their intended use. In medicine, law, finance, science, and similar domains, those judgments often require qualified professionals rather than general preference raters.

• AI Agent Evaluation by Professionals

  Agent evaluation assesses multi-step tool use, planning, and task completion rather than a single generated answer. A fluent final message can follow a harmful, wasteful, or incorrect trajectory.

• AI Benchmark Creation With Domain Experts

  Benchmark creation is the design of held-out tasks, scoring rules, and reference answers used to measure model capability over time. Expert benchmarks are written so items are difficult for the model and still scoreable by a professional standard.

• Expert Adjudication for AI Evaluation

  Expert adjudication is senior review that resolves disagreement between qualified reviewers and produces a final accepted label or score. It turns conflict into a standard instead of averaging it away.

• AI Evaluation Rubric Development

  Rubric development defines the criteria, examples, severity scales, and decision rules reviewers use so evaluation stays consistent. A credentialed cohort still produces noise if the standard is unspoken.

• Post-Training Data From Domain Experts

  Post-training data is expert-created material used after pretraining: supervised fine-tuning pairs, preference rankings, critiques, and RLHF support labels. The point is to teach or select a professional standard, not the average of the public web.