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Professional knowledge

Domain-expert AI training data.

Practitioner-created tasks and review criteria for medicine, law, finance, engineering, and other professional fields.

Discuss your use case

Bring professional judgment into model development.

Professional work depends on terminology, evidence standards, and context that vary by field. A specialist may need to account for a jurisdiction, a particular audience, or the limits of the source material.

Practitioners create tasks, example responses, preference comparisons, and evaluations. We define the specialty, intended users, inputs, and review standards before collecting examples.

Scope the expertise to the actual work

Identify the decisions the model will support and the expertise needed to assess them. Specify the specialty, jurisdiction, and application where relevant.

  • Specify the professional workflow and its constraints.
  • Define reviewer qualifications for the task.
  • Use representative documents and expected deliverables.

Preserve grounding and uncertainty

Review claims against the supplied evidence. Distinguish facts, assumptions, and missing information, including cases where the model should ask for more context.

  • Require support for source-dependent statements.
  • Review omissions as well as explicit errors.
  • Document disagreement and escalation criteria.

What a program can include

Scope can include:

  • Expert demonstrations or preference examples
  • Domain-specific review rubrics
  • Source references and reviewer rationales
  • Evaluation tasks aligned to the agreed scope

Illustrative task / Not a production record

Make the objective concrete.

Objective
Extract the required terms from a supplied professional document.
Context
The source document, extraction schema, and explicit review instructions.
Review
Check field accuracy, source references, completeness, and whether missing information is clearly marked.

Common questions

Can the same domain program include training and evaluation?

Yes. Plan separate collections for model learning and held-out evaluation, with shared quality definitions but distinct examples to make the evaluation meaningful.

How are domain requirements agreed?

Bring representative tasks, source materials, and acceptance criteria. The team can discuss the required expertise, review process, and deliverable format before collection begins.

Built around your model

What does your model need to do?

Share representative tasks and examples of a good result. We’ll scope the data and evaluation with you.

Discuss your use case