AI can help assemble a research argument. The argument still needs visible boundaries between source, interpretation, and uncertainty.
A fluent paragraph can make several kinds of claims sound equally established. One sentence describes a reported change. Another supplies a possible explanation. A third predicts what happens next. When those statements arrive in the same tone, the reader has to reconstruct the boundary between evidence and interpretation.
That boundary should be part of the research output. It is especially useful when AI helps draft a brief: a citation beside a paragraph does not mean that every conclusion in that paragraph appears in the cited source.
Give each claim a job
An observation describes something directly present in the material under review. An inference connects observations to an explanation or implication. An open question identifies what the available material does not resolve. These labels are a practical editorial convention, not a promise that a model can reliably classify every statement.
A disclosure that implementation services contributed a larger share of revenue supports an observation about the described revenue mix. A claim that the business is becoming more recurring requires additional evidence. The question of how much implementation work repeats without a new installation should remain open until the sources support an answer.
The distinction does not make inferences less valuable. Research depends on interpretation. It makes the argument easier to test because the reader can see which step requires judgment.
Test the citation at the claim level
For each consequential claim, open the supporting passage. Ask whether it supports the full sentence, including its scope, period, comparison, and degree of certainty. If a sentence makes two claims but the passage supports only one, split the sentence or add the missing evidence.
Check the surrounding context too. A statement of intent, a risk disclosure, and a reported result can contain similar words while supporting very different conclusions. Preserve the source type and document date in the review record.
A simple review can use four prompts:
- What exact observation does this passage support?
- What assumption connects that observation to the proposed inference?
- What plausible alternative explanation remains?
- What new evidence would weaken the inference?
Make uncertainty specific
“AI may be inaccurate” is too broad to help a researcher decide what to do next. “The source does not separate repeat implementation work from new deployments” identifies a useful limitation. Write missing information at that level.
The NIST AI Risk Management Framework provides a voluntary approach to managing AI risks; its generative AI profile addresses risks specific to generative systems. A practical research workflow can translate that broader risk perspective into concrete review steps, version records, and documented failure cases. Those are design choices for the workflow, not a claim that a particular output has been certified.
Review the process as well as the prose
Keep the source snapshot, the research question, the draft, and the reviewer’s revision connected. Record cases where an automated draft inferred too much, missed a qualification, or attached an irrelevant citation. Those examples are useful material for evaluating future changes to the workflow.
Avoid a confidence percentage unless its meaning and evaluation are defined. A precise-looking number can distract from a missing passage. A short explanation of the unresolved assumption usually gives the reviewer more to work with.
Gatani’s research automation engagements start by defining the output and human review point. The Gatani Lens research workspace keeps observations and next questions in separate fields, with links to the public source documents. The aim is a research argument that another person can examine, challenge, and improve.
This editorial collection was published on . Archive dates organize the series.