Expert data
Depth in the judgment.
Clarity in the data.
Expert feedback becomes useful training information when the task, evidence, and reasoning behind a judgment remain connected.
Judge the reasoning.
Keep the evidence.
- Task
- Compare two explanations of the same result.
- Criterion
- Does the conclusion follow from the supplied evidence?
- Feedback
- A preference, the decisive reason, and any uncertainty.
A planning example, not a product interface.
A label tells you the choice. Context helps explain it.
The formats and workflows below are illustrative planning examples.
Start with the decision the data should support.
Datlore brings vetted domain experts into the process of sourcing high-quality human feedback for frontier AI. The goal of a data program should be more precise than collecting opinions. A team might need to distinguish factual reliability from presentation quality, study the handling of uncertainty, or understand why a multi-step solution fails.
Each objective calls for a different observation. If the question is whether a solution is valid, reviewers need enough evidence to assess its reasoning. If the question is which response is more useful, they need a stated audience and purpose. A concise research question keeps the collection format tied to its intended use.
Think about what would change your next decision. Would a set of comparisons help select a training direction? Would structured error categories reveal a task-design problem? Would an explanation make an ambiguous assessment actionable? Define that relationship before choosing the size of the program.
Choose what the data needs to reveal
Match the format
to the question.
| Review format | The question it answers | What to retain |
|---|---|---|
| Comparative judgments | Which output better satisfies the brief? Allow a tie or insufficient evidence where appropriate. An expert may prefer a scientific explanation because it identifies a limiting assumption, rather than because it sounds authoritative. | The alternatives, the preference, and the decisive reason. |
| Rubric assessments | Which dimensions are strong or weak? Correctness, completeness, and constraint adherence can vary independently. A response may be accurate but unusable for its stated purpose. | The criterion definitions, dimension-level assessments, and relevant evidence. |
| Corrections and explanations | What should change, and why? In a software task, a reviewer might identify an unhandled input, explain the resulting failure, and suggest a bounded fix. Separate observation from untested speculation. | The specific error, its consequence, and the reasoning behind the correction. |
| Episode-level review | Were decisions justified as the task unfolded? Review the information available at each step and whether later success excuses an unsupported choice. See the episode walkthrough. | The sequence, the decisive step, and the basis for the assessment. |
A brief experts can actually use.
Good instructions reduce avoidable ambiguity while leaving room for informed disagreement.
A useful brief describes the user or research context, the material to review, the decision to make, and the evidence a reviewer may consult. It identifies constraints explicitly, including whether tools or outside references are permitted. It also says what to do when the material is incomplete.
For an illustrative mathematical review, the task might be to determine whether a proof establishes its stated result. The reviewer should know which assumptions are allowed, whether alternative proofs are in scope, and whether a minor notation error differs from a logical gap. Without these distinctions, two careful reviewers can produce incompatible labels for sensible reasons.
Include examples that clarify boundaries rather than simply repeating the instructions. An easy positive example is less informative than a near miss that looks convincing but violates one essential condition. Do not use examples to imply that every valid answer must follow a single style.
- State the research purpose and the exact review decision.
- Supply necessary context and permitted evidence.
- Define output fields and the meaning of each criterion.
- Provide an uncertainty or escalation path.
- Explain how disagreements will inform the next revision.
Quality is a question of fit.
Agreement can be useful, but it does not establish that the criteria are right. Experts can agree on an easy proxy that misses the intended capability. Review whether feedback captures the actual objective and whether different task variants expose different failure modes.
Begin with a focused sample and inspect it closely. Look for missing context, inconsistent rubric interpretations, overly broad categories, and rationales that merely restate a score. Preserve the version of the brief associated with a judgment so changes in instructions do not become unexplained changes in data.
When planning a program, discuss domain depth, task complexity, review expectations, data-use constraints, and evaluation needs. Datlore’s integrated platform connects expert data with task environments and reward-model tooling; the program conversation establishes how those pieces should fit your research.
Scope an expert-data programFurther reading: What makes expert feedback useful?