Expertise is necessary for many difficult reviews, but it is not sufficient by itself. An expert can make a careful judgment that is hard to use because the brief was ambiguous, the supporting evidence was missing, or the output format concealed the decisive reason. Useful feedback requires a relationship between the reviewer’s knowledge and the research question.
For an AI team, that relationship should remain visible after the review is complete. A preference, score, or correction needs enough context to explain what it means. Otherwise, apparent agreement can hide incompatible interpretations, and a dataset can become a collection of answers to slightly different questions.
The following approach is general planning guidance. The examples are illustrative and do not describe a particular Datlore program or promise a result.
1. Define the review decision.
Begin with what the expert is being asked to determine. “Is this a good response?” leaves too much unstated. Good for which user, with which evidence, under which constraints? A more useful decision might be whether a technical explanation supports its conclusion using only the provided material.
Separate the research objective from the immediate review action. The objective could be more reliable reasoning; the action could be identifying the first unsupported inference. That action is useful only if it exposes something relevant to the objective. Do not assume a convenient label captures the whole capability.
Specify what the reviewer should ignore as well as what matters. If writing style is outside the assessment, say so. If readability is important, define its relationship to correctness. Without this guidance, experts may make reasonable but incompatible choices about what to prioritize.
2. Give judgment an evidence base.
A reviewer needs the material required to make the decision. That could include the original brief, a source passage, allowed assumptions, an episode trace, or a clearly bounded reference set. More context is not always better; relevant context is the goal.
Make the evidence boundary explicit. Can the expert use outside knowledge? Are external references allowed? Should the review assess consistency with the supplied source, real-world truth, or both? These are different questions. A statement can faithfully summarize a flawed source while remaining false outside that context.
A useful rationale points to evidence that another reviewer can inspect. “This is more rigorous” is a conclusion. “This answer assumes independence even though the supplied setup specifies a dependency” identifies a reason. The second explanation helps a team understand both the error and the criterion being applied.
3. Choose a format that exposes the distinction.
Pairwise comparisons can reveal which response better meets a brief. Rubrics can separate dimensions. Corrections can locate a specific problem and describe a repair. Episode reviews can examine decisions over time. None is universally best; choose the format that preserves the information you need.
A forced preference is a poor fit when both responses are unacceptable for different reasons. A single score is a poor fit when a team needs to distinguish factual errors from missing context. An unrestricted essay can also be a poor fit if it produces interesting commentary without answering the review decision.
Combine structure with a focused explanation where useful. For instance, ask for the assessment, the decisive criterion, and a short evidence-based rationale. Avoid collecting fields merely because they are easy to add. Each field should support interpretation, quality review, or a concrete research decision.
4. Work through a concrete example.
Imagine two summaries of a supplied experiment. Summary A clearly states the observed effect but treats it as proof of a broad causal claim. Summary B describes the effect more cautiously and notes a limitation, but omits a secondary result. The reviewer must choose which better serves a brief asking for an accurate account of what the experiment establishes.
A weak review says, “B is better because it is more nuanced.” A stronger review identifies the specific overreach in A, connects it to the study design, and explains why the missing secondary result in B is less consequential under the stated criterion. That reasoning makes the preference interpretable.
Now change the brief to require complete extraction of all reported results. The same comparison may deserve a different assessment or a conclusion that neither response is adequate. The outputs have not changed; the review purpose has. Preserve the brief with the judgment so the preference is not later treated as context-free truth.
A review should not turn into an invented reference answer. If the source does not support a claim, the expert can identify that boundary without supplying facts from memory. The distinction between correcting a response and enriching it with new information should remain clear.
5. Preserve uncertainty as information.
Some tasks cannot be resolved from the available material. A brief may omit a necessary assumption, two solutions may be equally defensible, or the work may fall outside the reviewer’s expertise. These are meaningful outcomes, not automatically low-quality reviews.
Give uncertainty a reason. “Insufficient evidence” is more useful when it identifies what is missing and why that gap prevents the decision. “Outside my expertise” is more useful when it locates the part requiring a different specialist. A generic uncertain label can conceal as much as a forced confident one.
Do not penalize calibrated restraint by design. If every task demands a definitive answer, the data may reward confidence where the intended capability is recognizing limits. Decide in advance how ambiguity, abstention, and requests for clarification fit the research question.
6. Investigate disagreement before collapsing it.
Disagreement can arise from reviewer error, but it can also reveal competing interpretations, unclear priorities, or a real tradeoff. Start by reading the rationales. If two experts apply different assumptions because the brief is incomplete, averaging their labels will not repair the task.
Classify the cause before choosing a response. Missing context may call for a revised brief. Inconsistent rubric interpretation may call for better examples. A substantive difference in domain judgment may deserve further review and explicit preservation of the disagreement.
Agreement is also worth examining. A group of reviewers can consistently favor an attractive surface feature that does not correspond to the objective. Include examples that separate polish from correctness and familiar methods from valid alternatives. The goal is justified judgment, rather than agreement as an end in itself.
7. Inspect before expanding.
Review a focused sample closely before increasing the scale of collection. Look for whether a reader can reconstruct the decision from the task, the label, and the rationale. If those pieces do not align, more examples may reproduce the same ambiguity.
- Can the expert state the exact decision being made?
- Is the necessary evidence available and its boundary clear?
- Does the output format preserve the distinction you care about?
- Does the rationale identify a specific, inspectable reason?
- Can the reviewer express ties, uncertainty, or missing context?
- Have disagreements been examined for their causes?
- Are task and rubric revisions distinguishable in the resulting material?
Useful expert feedback is not simply a confident opinion from a knowledgeable person. It is a judgment whose purpose, evidence, and limits can be understood. That makes it easier to connect the feedback to reward signals and to revisit the task design when the evidence points to a mismatch.