Domain expertise
Knowledge is the beginning.
Judgment makes it useful.
The right expert contribution depends on the problem: what someone must notice, what they must weigh, and what they must be able to explain.
Define the expertise behind the decision.
Datlore brings vetted domain experts together with task environments and reward-model tooling.
Domain expertise matters when a response cannot be assessed by appearance alone. A plausible explanation may rely on a false assumption. A working implementation may fail on an important boundary condition. A tidy analysis may omit the evidence needed to justify its conclusion. Recognizing those differences takes knowledge applied to context.
For a research team, the useful question is not simply which discipline a reviewer belongs to. It is which decisions that reviewer is equipped to assess. Describe the subject matter, the level of difficulty, the practical context, and the consequences of common errors. That makes the expertise requirement specific enough to discuss.
The domain index below offers examples of how expert judgment can inform AI training. It is illustrative, not an exhaustive or verified list of available specialists. Availability and suitability for a particular program require a conversation about the actual task.
01 / Illustrative domain
Science
Scientific review can involve assessing whether evidence supports a claim, whether an experiment distinguishes competing explanations, or whether a proposed method respects its assumptions. Relevant expertise may be highly specific: knowing a field broadly does not always equip someone to judge a specialized technique.
An illustrative task could compare two interpretations of a supplied experiment. A reviewer would identify which conclusions follow from the observations, which require additional assumptions, and what uncertainty remains. The value lies in the reasoning behind that assessment, rather than confidence of presentation.
02 / Illustrative domain
Software
Software expertise can help distinguish code that appears reasonable from code that meets a brief under the stated constraints. Review may consider behavior, edge cases, maintainability, or the choice of a debugging action. Those are related questions, but they should not be treated as interchangeable.
A task might ask an expert to review a patch against a documented failure. The brief should identify the intended environment, permissible changes, and evidence needed to establish a fix. A strong reviewer can also say when the supplied context is insufficient to determine correctness.
03 / Illustrative domain
Mathematics
Mathematical judgment often concerns validity across a chain of reasoning. A correct final answer may conceal an unjustified step; a different method may be valid even when it departs from a reference solution. Tasks should make space for those distinctions.
For example, a reviewer could locate the first unsupported inference in a proof and explain which condition would make it valid. The required expertise depends on the subject and depth of the argument. Clear assumptions and a well-defined claim make that assessment more meaningful.
04 / Illustrative domain
Professional domains
Applied professional reasoning involves constraints, incomplete information, and tradeoffs. A useful task may ask whether an analysis fits a stated scenario, what evidence is missing, or which next step is defensible. Define the context precisely rather than asking for a universal “best” answer.
Such examples concern research and evaluation. They do not establish that a model is suitable for independent professional decision-making or replace the responsibilities of qualified practitioners. Programs should set boundaries around sensitive information and real-world reliance.
Depth, alignment,
and uncertainty.
A qualification is one input to understanding expertise; task alignment is another. Consider which kinds of problems a person has worked on, what evidence they routinely use, and whether they can communicate the distinctions your rubric needs. A task can require excellent diagnostic judgment without requiring the broadest possible credentials.
Make uncertainty a legitimate response. An expert who identifies a missing assumption or declines to infer an unsupported fact may be contributing exactly the signal a team needs. If every task forces a confident choice, the dataset can erase a valuable boundary between knowledge and speculation.
Before beginning, prepare a representative task and ask what an informed assessment would contain. Include a near miss, a genuinely ambiguous case, and an example that requires the intended depth of knowledge. These help expose whether the brief and the expertise requirement point at the same goal.
Bring the task to the conversation.
Describe the capability you are studying, the subject matter involved, the review decision, and the evidence an expert can use. Read about expert-data planning, or explore contributing as an expert.
Discuss your expertise needs